Interuniversity Graduate School of
Psychometrics and Sociometrics
-
Leiden University
University of Amsterdam
University of Groningen
Twente University
Tilburg University
Utrecht University
K.U.Leuven
Annual report 2010
© October 2011, Leiden, IOPS
Interuniversity Graduate School of Psychometrics and Sociometrics
Faculty of Social Sciences, Leiden University
P.O. Box 9555, 2300 RB Leiden, The Netherlands
Voice
E-mail
URL
071 527 3829
iops@iops.nl
www.iops.nl
Contents
Contents
Introduction
1
1
Organization
3
1.1
1.2
1.3
1.4
3
4
4
7
2
Staff
2.1
2.2
2.3
2.4
2.5
2.6
3
Board
Office
List of participating institutes
List of cooperating institutes
9
Staff meetings
Staff changes
Number of staff members
List of staff members
List of associated staff members
List of honorary emeritus members
Scientific awards and grants
3.1
3.1.1
3.1.2
3.1.2.1
3.1.2.2
3.1.2.3
3.1.3
3.1.4
3.1.5
3.2
3.2.1
3.2.2
Awards and grants honored to IOPS staff members
Scientific awards
NWO grants
NWO Veni, Vidi, Vici grants
NWO Aspasia grants
NWO Open Competition grants
International grants
Grants awarded to K.U.Leuven
Other grants
Awards and grants honored to IOPS PhD students
Scientific awards
Grants
9
9
10
10
15
16
17
17
17
17
17
19
20
22
23
24
28
28
28
IOPS annual report 2010
4
Students and projects
4.1
4.2
5
Graduate training program
5.1
5.2
5.2.1
5.2.2
6
7
Status of projects
Summary of projects
Courses in the IOPS curriculum
Conferences
25th IOPS summer conference
20th IOPS winter conference
29
29
31
91
91
91
91
92
Publications
95
6.1
6.1.1
6.1.2
6.2
6.3
6.4
6.5
6.6
6.7
6.8
95
95
96
96
108
111
111
112
113
113
Dissertations
Dissertations by IOPS PhD students
Other dissertations under supervision of IOPS staff members
Articles in international English-language journals
Contributions to international English-language volumes
Book reviews
Books and test manuals
Articles in other jounals
Software and test manuals
Other publications
Finances
7.1
7.2
7.3
Financial statement 2010
Summary of receipts and expenditures in 2010
Balance sheet 2010
115
115
116
116
Introduction
This report presents the activities, achievements and resources of the Interuniversity Graduate School of
Psychometrics and Sociometrics (IOPS) for the year 2010.
As usual, IOPS had a Summer conference (June 2010) and a Winter conference (December 2010), which
were organized by the University of Amsterdam and by Utrecht University, respectively. Two specialized
seminars targeted at IOPS PhD students were organized (by Leuven and by Twente).
In 2010, 7 PhD projects were successfully completed with a thesis, 10 new projects were started, 3 projects
were continuing beyond the original time limit, and 1 project was left unfinished. On December 31, 2010,
44 PhD projects were still in progress. IOPS was happy to welcome 4 new junior staff members, 1 new
senior staff member, while 4 senior and 1 junior staff members left IOPS. The total amount of staff reached
101 by the end of the year 2010.
Sadly, 2010 was the year that one of our senior staff members from Utrecht University, Dr Cora Maas,
passed away. She was an excellent teacher and a nice and compassionate colleague, who will be missed
dearly.
Of all grants that were awarded in 2010 to one of our staff members, the most noteworthy are the VENI
grant for Richard Morey, the VIDI grant for Ellen Hamaker, the VICI grant for Jeroen Vermunt, a SURF grant
for digital testing awarded to Han Van der Maas, and an international grant awarded by the Law School
Admission Council to Bernard Veldkamp. The best IOPS PhD student paper was won by by Jorge Tendeiro.
The Psychometric Society Dissertation prize for the best PhD thesis worldwide, was again for an IOPS PhD
student, Rinke Klein Entink.
In summary, IOPS continues to be a strong and inspiring platform for psychometricians and other quantitative behavioral scientists, to meet and to learn from each other.
Willem J. Heiser, scientific director, October 10, 2011
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IOPS annual report 2010
2
1 Organization
1
Organization
1.1
Board
The IOPS Board consists of seven members delegated by the participating universities. At most three
representatives of other research institutes may be appointed as an IOPS board member. Futhermore, the
institute director and the dissertation students' representative attend the board meetings.
On 31 December 2010 the IOPS Board consisted of:
- Prof. dr. W.J. Heiser, Chair, Leiden University
- Dr. M.J. De Rooij, Leiden University
- Dr. D. Borsboom, University of Amsterdam
- Prof. dr. R.R. Meijer, University of Groningen
- Prof. dr. H. Kelderman, VU University Amsterdam
- Dr. G.J.A. Fox, Twente University
- Dr. L.A. Van der Ark, Tilburg University
- Prof. dr. H.J.A. Hoijtink, Utrecht University
- Prof. dr. F. Tuerlinckx, K.U.Leuven
- Dr. A.A. Béguin, CITO (National Institute for Educational Measurement)
- Prof. dr. J.G. Bethlehem, CBS (Statistics Netherlands)
Director
Prof. dr. W.J. Heiser, Leiden University.
PhD representative
In the PhD student meeting of 11 December 2009, it was decided that Floryt van Wesel (Utrecht University)
will replace Ralph Rippe (Leiden University) as first PhD representative for the period 1 January 2010 - 31
December 2010. She was assisted by Jesper Tijmstra (Utrecht University), who served as asistant PhD
representative for a period of one year (1 January 2010 - 31 december 2010). Tijmstra will be appointed as
first representative as of 1 January 2011, for a period of one year.
Changes in the IOPS Board
In 2010, the following changes were made in the IOPS Board:
Dr. L.A. Van der Ark replaced prof. dr. J.A.P. Hagenaars (Tilburg University);
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IOPS annual report 2010
Board meetings
The IOPS Board meets four times a year. In 2010 IOPS Board only three meetings were held, in April, June,
and October 2010.
1.2
Office
Since 1 October 2000 the IOPS Graduate School holds office at Leiden University. The secretariat is accommodated at:
Institute of Psychology
Faculty of Social and Behavioral Sciences
Leiden University
P.O. Box 9555, 2300 RB Leiden, The Netherlands
Secretary
E-mail
Voice
URL
1.3
Susañña Verdel
iops@iops.nl
071 527 3829
www.iops.nl
List of participating institutes
Leiden University
Department of Psychology
Methodology and Statistics Unit
P.O. Box 9555, 2300 RB Leiden
Secretary
Jacqueline Dries
Voice
071 527 3761
E-mail
j.dries@fsw.leidenuniv.nl
Department of Education
Education and Child Studies
Faculty of Social and Behavioural Sciences
P.O. Box 9555, 2300 RB Leiden
Secretary
Esther Peelen
Voice
071 527 3434
E-mail
peelene@@fsw.leidenuniv.nl
4
1 Organization
University of Amsterdam
Psychological Methods
Department of Psychology
Roetersstraat 15, 1018 WB Amsterdam
Secretary
Ineke van Osch
Voice
020 525 6870
E-mail
w.h.m.vanosch@uva.nl
Developmental Psychology
Department of Psychology
Roetersstraat 15, 1018 WB Amsterdam
Secretary
Ellen Buijn
Voice
020 525 6830
E-mail
e.buijn@uva.nl
Work and Organizational Psychology
Department of Psychology
Roetersstraat 15, 1018 WB Amsterdam
Secretary
Joke Vermeulen
Voice
020 525 6860
E-mail
j.h.vermeulen@uva.nl
University of Groningen
Department of Psychology
Faculty of Behavioural and Social Sciences
Grote Kruisstraat 2/1, 9712 TS Groningen
Secretary
Hanny Baan
Voice
050 363 63 66
E-mail
j.m.baan@rug.nl
Department of Sociology
Faculty of Behavioural and Social Sciences
Grote Rozenstraat 31
9712 TG Groningen
Secretary
Saskia Simon
Voice
050 363 6469
E-mail
s.simon@rug.nl
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IOPS annual report 2010
Twente University
Department of Educational Measurement and Data Analysis
Faculty of Educational Science and Technology
P.O. Box 217, 7500 AE Enschede
Secretary
Birgit Olthof-Regeling
Voice
053 489 3555
E-mail
j.b.olthof@utwente.nl
Tilburg University
Department of Methodology and Statistics
Tilburg School of Social and Behavioral Sciences
P.O. Box 90153, 5000 LE Tilburg
Secretary
Marieke Timmermans
Voice
013 466 2544
E-mail
m.c.c.timmermans@uvt.nl
Utrecht University
Department of Methodology and Statistics
Faculty of Social and Behavioural Sciences
P.O. Box 80.140, 3508 TC Utrecht
Secretary
Flip Boerland / Chantal Molnar-van Velde
Voice
030 253 4438
E-mail
M.A.J.Boerland@uu.nl / c.molnar@uu.nl /
K.U.Leuven, Belgium
Department of Psychology
Research Group of Quantitative Psychology and Individual Differences
Tiensestraat 2B, B-3000 Leuven, Belgium
Secretary
Jasmine Vanuytrecht
Voice
+32 16 32 60 12
E-mail
Jasmine.Vanuytrecht@psy.kuleuven.be
6
1 Organization
1.4
List of cooperating institutes
University of Amsterdam
Department of Education
Faculty of Social and Behavioural Sciences
Nieuwe Prinsengracht 130, 1018 VZ Amsterdam
Secretary
Welmoed Torensma
Voice
020 525 1230 / 1201
E-mai
w.a.torensma@uva.nl
Secretary
Voice
E-mai
Janneke Aben
020 525 1559 / 1201
j.p.aben@uva.nl
VU University Amsterdam
Department of Social and Organizational Psychology
Faculty of Psychology and Pedagogics
Van der Boechorststraat 1, 1081 BT Amsterdam
Secretary
Anna Brinkman
Voice
020 598 8700
E-mail
aga.brinkman@psy.vu.nl
Secretary
Voice
E-mail
Dolly Manuputty
020 598 8717
dr.manuputty@psy.vu.nl
University of Groningen
Department of Education
Faculty of Behavioural and Social Sciences
Grote Rozenstraat 38, 9712 TJ Groningen
Secretary
Ina Doolsema
Voice
050 363 6540
E-mai
e.doolsema@rug.nl
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IOPS annual report 2010
Leiden University
Statistical Science for the Life and Behavioral Sciences
Mathematical Institute
P.O. Box 9512, 2300 RA Leiden
Secretary
Ellen Imthorn
Voice
071 527 7111
Maastricht University
Department of Methodology and Statistics
Faculty of Health, Medicine and Life Sciences
P.O. Box 616, 6200 MD Maastricht
Secretary
Marga Doyle
Voice
043 388 2395
E-mail
marga.doyle@maastrichtuniversity.nl
Erasmus University Rotterdam
Department of Econometrics
P.O. Box 1738, 3000 DR. Rotterdam
Secretary
Tineke Kurtz
Voice
010 408 1278 / 1259
E-mail
kurtz@few.eur.nl
Psychology Institute
P.O. Box 1738, 3000 DR. Rotterdam
Secretary
Hanny Langedijk, Susan Schuring
Voice
010 408 8789 / 8799
E-mail
langedijk@fsw.eur.nl, schuring@fsw.eur.nl
Wageningen University
Research Methodology Group
P.O. Box 8130, 6700 EW, Wageningen
Secretary
Aicha el Makoui
Voice
0317 48 4452
E-mail
office.enp@wur.nl
8
2 Staff
The members of the staff belong to the participating universities. There are two categories of staff
members: junior and senior staff members. Both require acknowledgment in their field according to,
among others, international publications. Junior staff members have obtained their PhD less than five years
ago, and do not necessarily have (co-)responsibility of dissertation research. Senior staff members do have
(co-)responsibility of dissertation research. The Dutch research institutes are evaluated by the KNAW (Royal
Dutch Academy for Sciences) every six years. IOPS staff members are evaluated by the IOPS Board before
the KNAW evaluation.
Associated staff
In 1994, the establishment of graduate schools and the rearrangement of staff members as a result of this,
caused IOPS to introduce a new category of staff for those who - for formal reasons - could not be a regular
IOPS staff member. The requirements for associated staff members are identical to those of regular staff
members. Dissertation students of these associated staff members can be admitted to IOPS as an external
dissertation student.
2.1
Staff meetings
Plenary meetings for all IOPS members (staff and PhD students) are held twice a year during the IOPS
conferences. In 2010 two plenary meetings took place, one on 10 June and one on 16 December 2010.
2.2
Staff changes
Junior staff members admitted to IOPS in 2010
-
Dr. Johan Braeken, Tilburg University
Dr. Fetene Tekle, Tilburg University
Dr. Rens Van der Schoot, Universiteit Utrecht
Dr. Wobbe Zijlstra, Tilburg University
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IOPS annual report 2010
Junior staff members leaving IOPS in 2010
- Dr. Rinke Klein Entink, Twente University
Senior staff members admitted to IOPS in 2010
- Prof. dr. Theo Eggen, Twente University
Senior staff members leaving IOPS in 2010
- Prof. dr. Jos Ten Berge, University of Groningen. Prof Ten Berge became an IOPs honorary emeritus
member.
- Prof. dr. Ab Mooijaart . Prof Mooijaart became an IOPs honorary emeritus member.
- Dr. Eeke Van der Burg
- Dr. Cora Maas (deceased)
2.3
Number of staff members
On 1 January 2010, the IOPS staff consisted of 99 members:
10 junior staff members
6 associated junior staff members
61 senior staff members
14 associated senior staff members
8 honorary emeritus members
On 31 December 2010, the IOPS staff consisted of 101 members:
13 junior staff members
6 associated junior staff members
56 senior staff members
16 associated senior staff members
10 honorary emeritus members
2.4
List of staff members
Staff members at Leiden University
Department of Psychology, Methodology and Statistics Unit
- Dr. Mark De Rooij (senior)
voice:071 527 4102, email: rooijm@fsw.leidenuniv.nl
- Prof. dr. Willem Heiser (senior)
voice: 071 527 3828, email: heiser@fsw.leidenuniv.nl
10
2 Staff
- Dr. Rien Van der Leeden (senior)
voice: 071 527 3763, email: leeden@fsw.leidenuniv.nl
- Dr. Kees Van Putten (senior)
voice: 071 527 3378, email: putten@fsw.leidenuniv.nl
Department of Education and Child Studies
- Prof. dr. Pieter Kroonenberg (senior)
voice: 071 527 3446, email: kroonenb@fsw.leidenuniv.nl
- Dr. Joost Van Ginkel (junior)
Voice: 071 527 3620, email : jginkel@fsw.leidenuniv.nl
Statistical Science for the Life and Behavioral Sciences
- Prof. dr. Jacqueline Meulman (senior)
voice: 071 527 7135, email: jmeulman@math.leidenuniv.nl
Staff members at University of Amsterdam
Department of Methodology
- Dr. Denny Borsboom (senior)
voice: 020 525 6882, email: d.borsboom@uva.nl
- Prof. dr. Paul De Boeck (senior)
voice: 020 5256923, email: p.a.l.deboeck@uva.nl
- Dr. Conor Dolan (senior)
voice: 020 525 6775, email: c.v.dolan@uva.nl
- Dr. Raoul Grasman (senior)
voice: 020 525 6738, email: r.p.p.p.grasman@uva.nl
- Dr. Pieter Koele (senior)
voice: 020 525 6881, email: p.koele@uva.nl
- Prof. dr. Han Van der Maas (senior)
voice: 020 525 6678, email: h.l.j.vandermaas@uva.nl
- Porf. dr. Gunter Maris (senior)
voice: 020 525 6677 email: g.k.j.maris@uva.nl
- Dr. Eric-Jan Wagenmakers (senior)
voice: 020 525 6420, email: e.m.wagenmakers@uva.nl
- Dr. Lourens Waldorp (junior)
voice: 020 525 6420, email: l.j.waldorp@uva.nl
- Dr. Jelte Wicherts (junior)
voice: 020 525 6738, email: j.m.wicherts@uva.nl
Department of Developmental Psychology
- Dr. Hilde Huizenga (senior)
voice: 020 525 6826, email: h.m.huizenga@uva.nl
- Dr. Brenda Jansen (senior)
voice: 020 525 6735, email: b.r.j.jansen@uva.nl
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IOPS annual report 2010
- Dr. Maartje Raijmakers (senior)
voice: 020 525 6826, email: m.e.j.raijmakers@uva.nl
- Dr. Ingmar Visser (senior)
voice: 020 525 6757, email: i.visser@uva.nl
Department of Work and Organizational Psychology
- Dr. Arne Evers (senior)
voice: 020 525 6751, email: a.v.a.m.evers@uva.nl
Staff members at University of Groningen
Department of Psychology
- Dr. Casper Albers (senior)
voice: 050 363 8239, email: c.j.albers@rug.nl
- Prof. dr. Henk Kiers (senior)
voice: 050 363 6339, email: h.a.l.kiers@rug.nl
- Prof. dr. Rob Meijer (senior)
voice: 050 363 6339, email: r.r.Meijer@rug.nl
- Dr. Richard Morey (junior)
voice: 050 363 7021, email: r.d.morey@rug.nl
- Dr. Alwin Stegeman (senior)
voice: 050 363 6193, email: a.w.stegeman@rug.nl
- Dr. Marieke Timmerman (senior)
voice: 050 363 6255, email: m.e.timmerman@rug.nl
Department of Sociology
- Dr. Anne Boomsma (senior)
voice: 050 363 6187, email: a.boomsma@rug.nl
- Dr. Mark Huisman (senior)
voice: 050 363 6345, email: j.m.e.huisman@rug.nl
- Dr. Marijtje Van Duijn (senior)
voice: 050 363 6195, email: m.a.j.van.duijn@rug.nl
Staff members at Twente University
Department of Educational Measurement and Data Analysis
- Prof. dr Theo Eggen (senior)
voice: 053 489 3574, email: t.j.h.m.eggen@gw.utwente.nl
- Dr. ir. Jean-Paul Fox (senior)
voice: 053 489 3326, email: foxj@edte.utwente.nl
- Prof. dr. Cees Glas (senior)
voice: 053 489 3565, email: glas@edte.utwente.nl
12
2 Staff
- Dr. ir. Bernard Veldkamp (senior)
voice: 053 489 3653, email: veldkamp@edte.utwente.nl
- Dr. ir. Hans Vos (senior)
voice: 053 489 3628, email: vos@edte.utwente.nl
Staff members at Tilburg University
Department of Methodology and Statistics
- Dr. Johan Braeken, (junior)
voice: 013 466 2275, email: j.braeken@uvt.nl
- Dr. Marcel Croon (senior)
voice: 013 466 2284, email: m.a.croon@uvt.nl
- Dr. Wilco Emons (junior)
voice: 013 466 2397, email: w.h.m.emons@uvt.nl
- Dr. John Gelissen (senior)
voice: 013 466 2974, email: j.p.t.m.gelissen@uvt.nl
- Dr. Guy Moors, Tilburg University (senior)
voice: 013 466 2249, email: guy.moors@uvt.nl
- Prof. dr. Klaas Sijtsma (senior)
voice: 013 466 3222, email: k.sijtsma@uvt.nl
- Dr. Fetene Tekle (junior)
voice: 013 466 2959, email: f.b.tekle@uvt.nl
- Dr. Marcel Van Assen (junior)
voice: 013 466 2362, email: m.a.l.m.vanassen@uvt.nl
- Dr. Andries Van der Ark (senior)
voice: 013 466 2748, email: a.vdark@uvt.nl
- Prof. dr. Jeroen Vermunt (senior)
voice: 013 466 2748, email: j.k.vermunt@uvt.nl
- Dr. Matthijs Warrens (junior)
voice: 013 466 3270, email: m.j.warrens@uvt.nl
- Dr. Wobbe Zijlstra (junior)
voice: 013 466 3005, email: w.p.zijlstra@uvt.nl
Staff members at Utrecht University
Methodology & Statistics Department
- Dr. Hennie Boeije (senior)
voice: 030 253 7983, email: h.boeije@uu.nl
- Dr. Maarten Cruyff (junior)
voice: 030 253 9235, email: m.cruyff@uu.nl
- Dr. Edith De Leeuw (senior)
voice: 030 253 7983, email: e.d.deleeuw@uu.nl
- Dr. Laurence Frank (junior)
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IOPS annual report 2010
voice: 030 253 9237, email: l.e.frank@uu.nl
- Dr. Ellen Hamaker (senior)
voice: 030 253 1851, email: e.l.hamaker@uu.nl
- Dr. David Hessen (senior)
voice: 030 253 1491, email: d.j.hessen@uu.nl
- Prof. dr. Herbert Hoijtink (senior)
voice: 030 253 9137, email: h.hoijtink@uu.nl
- Prof. dr. Joop Hox (senior)
voice: 030 253 9236, email: j.hox@uu.nl
- Dr. Irene Klugkist (senior)
voice: 030 253 5473, email: i.klugkist@uu.nl
- Dr. Gerty Lensvelt-Mulders (senior)
voice: 030 253 5857, email: g.j.l.m.lensvelt@uu.nl
- Dr. Gerard Maassen (senior)
voice: 030 253 4765, email: g.h.maassen@uu.nl
- Dr. ir. Mirjam Moerbeek (senior)
voice: 030 253 1450, email: m.moerbeek@uu.nl
- Prof. dr. Ger Snijkers (senior)
voice: 030 253 4688, email: g.snijkers@uu.nl
- Prof. dr. Stef Van Buuren (senior)
voice: 030 253 6707, email: s.vanbuuren@uu.nl
- Prof. dr. Peter Van der Heijden (senior)
voice: 030 253 4688, email: p.g.m.vanderheijden@uu.nl
- Dr. Rens Van der Schoot (junior)
voice: 030 253 1571, email: a.g.j.vandeschoot@uu.nl
Staff members at K.U.Leuven
Department of Psychology
- Dr. Eva Ceulemans (senior)
voice: +32 16 32 6108, email: eva.ceulemans@ psy.kuleuven.be
- Dr. Yuri Goegebeur (senior)
voice: +32 16 32 6762, email: yuri.goegebeur@ psy.kuleuven.be
- Prof. dr. Francis Tuerlinckx (senior)
voice: +32 16 32 5999, email: francis.tuerlinckx@psy.kuleuven.be
- Prof. dr. Iven Van Mechelen (senior)
voice: 32 16 31 6131, email: iven.vanmechelen@ psy.kuleuven.be
14
2 Staff
2.5
List of associated staff members
- Dr. Lidia Arends (junior), Erasmus University, Erasmus University Rotterdam
voice: 010 408 8667, email: arends@fsw.eur.nl
- Dr. Timo Bechger (senior), CITO (National Inst. for Educational Measurement), Arnhem
voice: 026 352 1162, email: timo.bechger@cito.nl
- Dr. Anton Béguin (senior), CITO (National Inst. for Educational Measurement), Arnhem
voice: 026 352 1042, email: anton.beguin@cito.nl
- Prof. dr. Martijn Berger (senior), Methodology and Statistics, Maastricht University
voice: 043 388 2258, email: martijn.berger@maastrichtuniversity.nl
- Prof. dr. Jelke Bethlehem (senior), CBS (Statistics Netherlands), Den Haag
voice: 070 337 3800, email: jbtm@cbs.nl
- Dr. Samantha Bouwmeester (junior), Erasmus University Rotterdam
voice: 010 408 8657, email: bouwmeester@fsw.eur.nl
- Dr. Math Candel (senior), Methodology and Statistics, Maastricht University
voice: 043 388 2273, email: math.candel@maastrichtuniversity.nl
- Dr. ing Paul Eilers (senior), Department of Biostatistics, Erasmus Medical Center Rotterdam
voice: 010 704 3792, email: p.eilers@erasmusmc.nl
- Prof. dr. Patrick Groenen (senior), Faculty of Economics, Erasmus University Rotterdam
voice: 010 408 1281, email: groenen@few.eur.nl
- Dr. Bas Hemker (junior), CITO (National Inst. for Educational Measurement), Arnhem
voice: 026 352 1329, email: bas.hemker@cito.nl
- Dr. Margo Jansen (senior), Department of Education, University of Groningen
voice: 050 363 6540, email: g.g.h.jansen@rug.nl
- Prof. dr. Henk Kelderman (senior), Dept. of Social and Organizational Psy., VU University Amsterdam
voice: 020 598 8715, email: h.kelderman@psy.vu.nl
- Dr. Niels Smits (senior), Department of Social and Organizational Psychology, VU University Amsterdam
voice: 020 598 8713, email: n.smits@psy.vu.nl
- Dr. Yfke Ongena (junior), Communication Studies, University of Groningen
voice: 050 363 9607, email: y.p.ongena@rug.nl
- Dr. Frans Oort (senior), Department of Education, University of Amsterdam
voice: 020 525 1314, email: f.j.oort@uva.nl
- Prof. dr. Tom Snijders (senior), Department of Sociology, University of Groningen
voice: 050 363 6188, email: t.a.b.snijders@rug.nl
- Dr. Frans Tan (junior), Methodology and Statistics, Maastricht University
voice: 043 388 2278, email: frans.tan@maastrichtuniversity.nl
- Dr. Hilde Tobi (senior), Research Methodology, Wageningen University
voice: 0317 485 946, email: hilde.tobi@wur.nl
- Dr. Gerard Van Breukelen (senior), Methodology and Statistics, Maastricht University
voice: 043 388 2274, email: gerard.vbreukelen@maastrichtuniversity.nl
- Dr. Wijbrandt Van Schuur (senior), Department of Sociology, University of Groningen
voice: 050 363 6436, email: h.van.schuur@rug.nl
- Dr. Wolfgang Viechtbauer (senior), Methodology and Statistics, Maastricht University
voice: 043 388 2277, email: wolfgang.viechtbauer@maastrichtuniversity.nl
- Dr. Bonne Zijlstra (junior), Department of Education, University of Amsterdam
voice: 020 525 1242, email: b.j.h.zijlstra@uva.nl
15
IOPS annual report 2010
2.6
-
16
List of honorary emeritus members
Prof. dr. Wil Dijkstra, email: w.dijkstra@fsw.vu.nl
Prof. dr. Jacques Hagenaars, email: jacques.a.hagenaars@uvt.nl
Prof. dr. Gideon Mellenbergh, email: g.j.mellenbergh@uva.nl
Prof. dr. Robert Mokken, email: mokken@science.uva.nl
Prof. dr. Ivo Molenaar, email: w.molenaar@rug.nl
Prof. dr. Ab Mooijaart, email: mooijaart@fsw.leidenuniv.nl
Prof. dr. Willem Saris, email: w.saris@telefonica.net
Prof. dr. Jos Ten Berge, email: j.m.f.ten.berge@rug.nl
Prof. dr. Wim Van der Linden, email: wim_vanderlinden@ctb.com
Prof. dr. Hans Van der Zouwen, email: j.van.der.zouwen@fsw.vu.nl
Dr. Norman Verhelst, email: norman.verhelst@gmail.com
3
Scientific awards and grants
3.1
Awards and grants honored to IOPS staff members
3.1.1 Scientific awards
In 2010, the following IOPS staff members were honored with a scientific award:
- Arends, Lidia (2010), Erasmus University Rotterdam.
MORE Teaching prize 2009-2010. Medical Science, Erasmus Medical Center.
- He, Q. (2010), Twente University.
Psychometric Society Travel Award.
Psychometric Society 2010: Athens, Georgia, U.S.A. (6-9 July 2010).
- Snijders, Tom A.B. (2010), University of Groningen.
Georg Simmel Award, International Network for Social Network Analysis.
3.1.2 NWO grants
3.1.2.1
NWO Veni, Vidi, Vici grants
The Veni, Vidi, and Vici grants are part of the NWO Innovational Research Incentives Scheme [Vernieuwingsimpuls]. The followoing IOPS researchers were awarded:
- Borsboom, Denny (2007), University of Amsterdam
Grant:
Vidi grant
Project:
Causal networks for psychological measurement
Period:
1 March 2008 - 1 March 2013
Budget:
€ 600.000
- De Rooij, Mark (2006), Leiden University
Grant:
Vidi grant
Project:
Modelling individual differences in change patterns
Period:
1 September 2006 - 1 September 2011
Budget:
€ 405.600
17
IOPS annual report 2010
- Fox, Jean-Paul (2007), Twente University
Grant:
Vidi grant
Project:
Bayesian methodology for large-scale comparative research
Period:
1 December 2007 - 1 December 2012
Budget:
€ 600.000
- Grasman, Raoul, (2006), University of Amsterdam
Grant:
Veni grant
Project:
Multisubject analysis of EEG/MEG activity in anatomical connectivity graphs
Period:
1 Februray 2006 - 1 December 2010
Budget:
€ 208.000
- Hamaker, Ellen (2010), Utrecht University
Grant:
Vidi grant
Project:
Time for change: Studying individual differences in dynamics
Period:
1 May 2011 - 1 May 2016
Budget:
€ 800.000
- Hoijtink, Herbert (2005), Utrecht University
Grant:
Vici grant
Project:
Learning more from empirical data using prior knowledge
PhD students: Joris Mulder, Rebecca Kuiper, Carel Peeters, Rens van de Schoot, and Floryt van
Wesel
Period:
2006 - 2011
Budget:
€ 1.250.000
- Huizenga, Hilde (2006), University of Amsterdam
Grant:
Vidi grant
Project:
The association between intelligence and performance variability: a new statistical
neuroscientific approach
PhD student: Wouter Weeda
Period:
March 2006 - December 2010
Budget:
€ 405.600
- Moerbeek, Mirjam (2008), Utrecht University
Grant:
Vidi grant
Project:
Improving statistical power in studies on event occurrence by using an optimal design
Period:
1 February 2009 - 1 February 2014
Budget:
€ 600.000
- Morey, Richard (2010), University of Groningen
Grant:
Project:
Period:
Budget:
18
Veni grant
A modelling-based approach to testing item-based versus resource-based
working memory storage
1 May 2011 - 1 May 2014
€ 250.000
3 Scientific awards and grants
- Raijmakers, Maartje (2006), University of Amsterdam
Grant:
Vidi grant
Project:
The dynamics of rule learning in infants and preschoolers
Period:
1 April 2007 - 1 April 2012
Budget:
€ 405.600
- Stegeman, Alwin (2008), University of Groningen
Grant:
Vidi grant
Project:
Multi-way decompositions : Existence and uniqueness
Period:
6 February 2009 - 5 February 2014
Budget:
€ 600.000
- Vermunt, Jeroen (2010), Tilburg University
Grant:
Vici grant
Project:
Stepwise model-fitting approaches for latent class analysis and related methods
Period:
23 June 2011-22 June 2016 (5 years)
Budget:
€ 1.500.000
- Wagenmakers, Eric-Jan (2006), University of Amsterdam
Grant:
Vidi grant
Project:
Modeling the relation between speed and accuracy [Rot maar vlot].
Period:
1 June 2007 - 1 June 2012
Budget:
€ 600.000
- Wicherts, Jelte (2007), University of Amsterdam
Grant:
Veni grant
Project:
Measurement distortion in experimental psychology and how factor analysis can help
restore construct validity
Period:
1 June 2007 - 1 June 2012
Budget:
€ 208.000
3.1.2.2
NWO Aspasia grants
With the Aspasia grants, NWO stimulates the promotion of female researchers in higher ranking. The
following IOPS researchers were awarded:
- Moerbeek, Mirjam (2009), Utrecht University
Period:
1 February 2009 - 1 February 2014
Budget:
€ 100.000
- Raijmakers, Maartje (2006), University of Amsterdam
Period:
1 April 2007 - 2012
Budget:
€ 100.000
19
IOPS annual report 2010
3.1.2.3
NWO Open Competition grants
The Open Competion is subsidy programme for the advancement of innovative and high-quality scientific
research in the social and behavioural sciences. The following IOPS researchers received an Open
Competion grant by NWO (details of the research projects can be found in Chapter 4):
- Berger, Martijn (2006), Maastricht University
Project:
Optimal designs for fMRI experiments
PhD student: Bärbel Maus
Period:
1 October 2006 - 1 October 2010
Budget:
€ 176.714
- Boomsma, Anne, Van Duijn, Marijtje, & Timmerman, Marieke (2005), University of Groningen
Project:
A comparison between factor analysis and item response theory modeling in scale
construction.
PhD student: Janke Ten Holt
Period:
1 September 2005 - 1 November 2010
Budget:
169.529
- Borsboom, Denny (2006), University of Amsterdam
Project:
Admissible statistics and latent variable theory
PhD student: Annemarie Zand-Scholten
Period:
1 June 2006 - 1 September 2010
Budget:
€ 176.714
- De Rooij, Mark (2010), Leiden University
Project:
Multivariate logistic regression using the ideal point classification model
PhD student: Haile M. Worku
Period:
1 October 2010 - 1 October 2014
Budget:
€ 209.513
- Gelissen, John, & Jeroen Vermunt (2005), Tilburg University
Project:
Bias and equivalence in cross-cultural survey research: An analysis of instrument comparability in the SPVA survey
PhD student: Meike Morren
Period:
1 February 2007 - 1 February 2011
Budget:
€ 176.714
- Moerbeek, Mirjam (2006), Utrecht University
Project:
Robustness issues for cluster randomized trials.
PhD student: Elly Korendijk
Period:
1 September 2006 - 1 September 2011
Budget:
€ 181.348
20
3 Scientific awards and grants
- Moors, Guy, & Jeroen Vermunt (2006), Tilburg University
Project:
Question format and response style behaviour in attitude research
PhD student: Natalia Kieruj
Period:
1 September 2007 - 1 September 2011
Budget:
€ 181.871
- Sijtsma, Klaas, Wilco Emons, & Marcel Van Assen (2007), Tilburg University
Project:
Person-misfit in Item Response Models explained by means of nonparametric and multilevel logistic regression models
PhD student: Judith Conijn
Period:
2007 - 2012
Budget:
€ 181.871
- Sijtsma, Klaas, & Wilco Emons (2006), Tilburg University
Project:
Minimal requirements of the reliability of tests and questionnaires
PhD student: Peter Kruyen
Period:
15 November 2008 - 15 November 2012
Budget:
€ 181.871
- Timmerman, Marieke & Rob Meijer (2009), University of Groningen
Project:
Dimensionality assessment of polytomous Items
PhD student: M.T. Barendse
Period:
1 September 2010 - 1 September 2014
Budget:
€ 209.513
- Van der Ark, Andries, Marcel Croon, & Klaas Sijtsma (2008), Tilburg University
Project:
Test construction using marginal models
PhD student: Irena Mikolajun
Period:
1 January 2009 - 1 January 2013
Budget:
€ 186.995
- Van der Maas, Han (2006), University of Amsterdam
Project:
Development of cognitive expertise in chess
Proposers:
Han Van der Maas, Eric-Jan Wagenmakers, & Frenk Van Harreveld
PhD student: Daan Zult
Period:
1 November 2006 - 1 November 2010
Budget:
€ 170.000
- Vermunt, Jeroen, Andries Van der Ark, & Klaas Sijtsma (2009), Tilburg University
Project:
Multiple imputation using mixture models
PhD student: Daniël Van der Palm
Period:
1 September 2009 – 1 September 2013
Budget:
€ 207.155
21
IOPS annual report 2010
- Wagenmakers, Eric-Jan & Birte Forstmann (2008), University of Amsterdam
Project:
The anatomical and neurochemical foundations of decision-making under time pressure
Project leader: Birte Forstmann
PhD student: Jasper Winkel
Period:
1 April 2009 - 1 April - 2013
Budget:
€ 218.000
- Wagenmakers, Eric-Jan, Birte Forstmann, Sander Nieuwenhuis, Rafal Bogacz, Scott Brown, John Serences & Han van der Maas. (2010):
Project:
The neural basis of decision-making with multiple choice alternatives
Postdoc:
Martijn Mulder
Period:
01 June 2010 – 1 June 2013
Budget:
€ 231.635
- Wicherts, Jelte (2009), University of Amsterdam
Project:
Expectancy effects on the analysis of behavioral research data.
PhD student: Marjan Bakker
Period:
1 April 2009 - 1 April 2013
Budget:
€ 207.155
3.1.3 International grants
- Dijkstra, Wil (2005), VU University Amsterdam
Grant:
Grant by The Weinberg Group, Washington
Project:
Quality assurance of Data Collection Procedures for the C-TOR project
Period:
April 2005 - 2010 (with probable prolongation)
Budget:
€ 300.000
- Snijders, Tom (2006), University of Groningen
Grant:
Grant by NWO EUROCORES (European Science Foundation Collaborative
Research Programmes Scheme)
Project:
Models for the evolution of networks and behavior
Researcher:
C.E.G. Steglich
Period:
1 September 2006 - 1 September 2010
Budget:
€ 209.361
- Snijders, Tom (2006), University of Melbourne, Australia
Grant:
Discovery grant by the Australian Research Council (ARC)
Proposers:
Philippa Pattison, Garry Robins, & Tom Snijders
Project:
Statistical models for social networks, network-based social processes and complex social
systems.
Period:
2006 - 2010
Budget:
AUS $ 710.000
22
3 Scientific awards and grants
- Snijders, Tom (2008), University of Oxford, United Kingdom
Grant:
Grant by National Institutes of Health (USA). Grant number: 1R01HD052887-01A2
Principal investigator: John M. Light.
Project:
Adolescent peer social network dynamics and problem behavior
Sub-project carried out at the University of Oxford and led by Tom Snijders
Period:
2008 through 2012
Budget:
$ 711.324
3.1.4 Grants awarded to K.U.Leuven
- Ceulemans, Eva, Patrick Onghena (K.U.Leuven), and Marieke Timmerman, co-supervisor (University
of Groningen)
Grant:
Grant by The National Fund for Scientific Research - Belgium [Fonds voor Wetenschappelijk Onderzoek - Vlaanderen]
Project:
Componenten- en HICLAS-modellen voor de analyse van structuurverschillen in reëelwaardige en binaire multivariate multiniveau gegevens
Period:
1 January 2009 - 1 January 2013
Budget:
€ 278.725
- Janssen, Rianne, Francis Tuerlinckx, Wim Vanden Noortgate, & Bieke De Fraine (2006), K.U.Leuven
Grant:
Grant by Flemish Department of Education [Vlaams Ministerie van Onderwijs en Vorming]
Project:
Strategische Beleidsondersteuning: Periodic assessment of pupil performance in compulsory education
Period:
2006 - 2011
Budget:
€ 875.412
- Tuerlinckx, Francis (2008), K.U.Leuven
Grant:
Grant by The National Fund for Scientific Research - Belgium [Fonds voor Wetenschappelijk Onderzoek - Vlaanderen]
Project:
Niet-lineaire modellen voor affectdynamiek.
Period:
2008 - 2012
Budget:
€
- Van Mechelen, Iven, Francis Tuerlinckx, & Eva Ceulemans (2008), K.U.Leuven
Grant:
GOA
Project:
Formele modellering van de tijdsdynamiek van emoties
Period:
2008 - 2014
Budget:
€
23
IOPS annual report 2010
- Van Mechelen, Iven (2008), K.U.Leuven
Grant:
Grant by The National Fund for Scientific Research - Belgium [Fonds voor Wetenschappelijk Onderzoek - Vlaanderen]
Project:
Een koninklijke weg tot een beter begrip van de mechanismen onderliggend aan persoonlijkheidsgerelateerd gedrag
Period:
2008 - 2012
Budget:
€
- Research Group Quantitative Psychology (2006), K.U.Leuven
Grant:
Grant by Belgian Science Policy [Federaal Wetenschapsbeleid]
Project:
Statistical analysis of association and dependencies in complex data
Period:
2007 - 2011
Budget:
€ 80.000
- Research Group Quantitative Psychology (2006), K.U.Leuven
Grant:
Grant by Institute for the Promotion of Innovation by Science and Technology in
Flanders (IWT). [Instituut voor de Aanmoediging van Innovatie door Wetenschap &
Technologie in Vlaanderen]
Project:
Bioframe: An algorithmic framework for integrative modeling in systems biology
Period:
2007 - 2010
Budget:
€ 46.445
- Research Group Quantitative Psychology (2005), K.U.Leuven
Grant:
Grant by Research Fund K.U.Leuven [Onderzoeksfonds K.U. Leuven]
Project:
SymBioSys - KU Leuven Center for Computational Systems Biology
Period:
2005 - 2010
Budget:
€ 64.300 (approximately)
3.1.5 Other grants
- Boersma, P., Maartje Raijmakers, & S. Bögels, S. (2009), University of Amsterdam
Grant:
Cognition Program, Cognitive Science Center Amsterdam
Project:
Models and tests of early category formation: interactions between cognitive,
emotional, and neural mechanisms
Period:
Budget:
2009 - 1 September 2015
€ 470.000
- Boo, G. de, P. Prins, T.G. Van Manen, & Hilde Huizenga (2007), University of Amsterdam
Grant:
ZonMW, Programma “Jeugd: Vroegtijdige signalering & interventies”
Project:
Effectiveness of a stepped-care school-based intervention for children with disruptive
behavior disorders [Ontwikkeling en toetsing van een multisysteem interventieprogramma voor kinderen met gedragsproblemen uitgevoerd op school].
Period:
1 April 2008 - 1 May 2012
Budget:
€ 386.041
24
3 Scientific awards and grants
- Groeneveld, C. & Han Van der Maas (2010)
Grant:
SURF Foundation Tender: Toetsing en Toetsgestuurd Leren
Project:
Computer Adaptieve Monitoring in het statistiekonderwijs
Period:
1 maart 2011 - 28 maart 2013
Budget:
€ 348.821
- Klinkenberg, S. & Han Van der Maas (2010)
Grant:
SURF Foundation Tender: Toetsing en Toetsgestuurd Leren
Project:
Nieuwe scoreregel voor digitale toetsen
Period:
1 maart 2011 – 28 maart 2014
Budget:
€ 77.766
- Ruiter, S.A.J., B.F. Van der Meulen, Marieke Timmerman, & W. Ruijssenaars (2009), University of
Groningen
Grant:
ZonMW, Programma “Zorg voor Jeugd: Handelingsgerichte diagnostiek voor jonge
kinderen met cognitieve en/of functionele beperkingen”
Period:
2009 - 2013
Budget:
€ 449.510
- Glas, Cees (2006), Twente University
Grant:
Research cooperation with OECD
Project:
PISA (Program for International Student Assessment), Cycle 2009, Core B.
Period:
November 2006 - November 2010
Budget:
€ 100.000
- Glas, Cees (2006), Twente University
Grant:
NUFFIC PhD grant by Netherlands Organization for International Cooperation in Higher
Education (NUFFIC)
Project:
Item analysis of entry test for admission to BSc engineering
PhD student: Muhammad Naveed Khalid
Period:
March 2006 - March 2010
Budget:
€ 32.000
- Heiser, Willem, & Kees Van Putten, Leiden University. Norman Verhelst (2006), Cito
Grant:
Research cooperation, partly funded by Cito, Arnhem
Project:
Mathematical proficiency in primary education: Cognitive processes and predictability
PhD student: Marian Hickendorff
Period:
September 2006 - August 2010
- Klugkist, Irene and Kristel Janssen, (main applicants); Herbert Hoijtink, Carl Moons, (2009),
Utrecht University
Grant for PhD-project in Focus area Epidemiology, Utrecht University
Grant:
Period:
Budget:
September 2009 - August 2013
€ 210.000
25
IOPS annual report 2010
- Meijer, Rob (2006), Twente University
Grant:
Research cooperation with Dutch Bureau for Firefighter Examinations [NBBE,
Nederlands Bureau Brandweer Examens]
Project:
The development of virtual reality examinations for fire fighters [De ontwikkeling van
een virtual-reality-examen voor brandweerpersoneel]
Period:
November 2006 - November 2010
Budget:
€ 24.000
- Meijer, Rob (2006), Twente University
Grant:
Research cooperation with PiCompany
Project:
Development of online IRT based assessment instruments.
Period:
October 2006 - October 2010
Budget:
€ 180.000
- Ten Berge, Jos, & Henk Kiers (2006), University of Groningen
Grant:
PhD student grant by Fundação para a Ciência e a Tecnologia (FCT, Portugal)
Project:
Uniqueness, rank, and simplicity of three-way arrays with symmetric slices.
PhD student: Jorge Nunes Tendeiro
Period:
October 2006 - September 2010
- Van der Heijden, Peter (2009), Utrecht University
Grant:
Ministerie van VROM
Project:
Actualiseringsonderzoek schatting aantal in Nederland verblijvende Antilliaanse
Nederlanders die niet ingeschreven zijn bij de GBA
Period:
Budget:
2009 - 2010
€ 28.806
- Van der Heijden, Peter (2009), Utrecht University
Grant:
RWS DVS
Ontwikkeling Scheepsgevallenmonitor
Project:
Period:
2009 - 2010
Budget:
€ 19.140
- Van der Heijden, Peter (2009), Utrecht University
Grant:
CBS
Project:
Onderzoek schattingen van omvang van niet-GBA-bevolking en dak- en thuislo-
zen
Period:
Budget:
2009 - 2010
€ 82.319
- Veldkamp, Bernard (2006), Twente University
Grant:
Research Grant by PiCompany
Project:
PhD project Development of online IRT assessment instrument
Period:
2006 - 2010
Budget:
€ 200.000
26
3 Scientific awards and grants
- Veldkamp, Bernard (2010), Twente University
Grant:
Law School Admission Council
Project:
Data mining for testlet modeling and its applications
Period:
2010 - 2012
Budget:
€ 200.000
- Veldkamp, Bernard (2010), Twente University
Grant:
SASS
Project:
Implementation text mining based classification system for PTSD patients
Period:
2010 - 2011
Budget:
€ 70.000
- Veldkamp, Bernard (2010), Twente University
Grant:
ECABO
Project:
Quality of performance tests (PhD student project)
Period:
2010 - 2013
Budget:
€ 250.000
- Veldkamp, Bernard (2010), Twente University
Grant:
CZ
Project:
Automated fraud detection, con'd
Period:
2010 - 2010
Budget:
€ 15.000
- Viechtbauer, Wolfgang (2009), Maastricht University
Grant:
ZonMw
Principal Investigator: Marijn de Bruin
Project:
Determining the cost-effectiveness of an effective intervention to improve adherence
among treatment-experienced HIV-infected patients in the Netherlands
Period:
2009 - 2012
Budget:
€ 428.095
- Viechtbauer, Wolfgang (2009), Maastricht University
Grant:
Funded by Pfizer and the Stichting Gezondheidscentra Eindhoven.
Principal Investigator: Daniel Kotz
Project:
Helping more smokers to quit by COmbining VArenicline with COunselling for smoking
cessation: The COVACO randomized controlled trial
Period:
2009 - 2013
Budget:
€ 300.000
27
IOPS annual report 2010
3.2
Awards and grants honored to IOPS PhD students
3.2.1 Scientific awards
In 2010, the following IOPS PhD students were honored with a scientific award:
- Tendeiro, J.N. (2010). IOPS PhD Student Best Paper Award 2009.
Paper: Tendeiro, J.N., Ten Berge, J.M.F., & Kiers, H.A.L. (2009). Simplicity transformations for three-way
arrays with symmetric slices, and applications to Tucker-3 models with sparse core arrays. Linear Algebra & Applications, 430, 924–940.
- Klein Entink, R.H. (2010). Psychometric Society Dissertation Award.
IMPS 2010: The 75th Annual Meeting of the Psychometric Society. Athens, Georgia, U.S.A. (July 2010).
- Rippe, R. (2010). Best Student Presentation Award.
Title of presentation: Efficient semi-parametric genotyping of SNP arrays.
25th International Workshop on Statistical Modeling, Glasgow (July 2010).
3.2.2 Grants
- Molenaar, Dylan (2007), University of Amsterdam
Grant:
NWO Top talent Grant
Project:
Statistical modeling of (cognitive) ability differentiation
Period:
1 September 2007 - 1 September 2011
28
4
Students and projects
Applicants for the IOPS dissertation training must have a Master's degree in one of the following disciplines. Behavioral Sciences, Technical Sciences, Mathematics or Econometrics. They are appointed as PhD
student, or as an indirectly financed PhD student. PhD students within IOPS are financed by the
participating universities or by NWO (Netherlands Foundation of Scientific Research).
The annual report of 2009 reported a total of 41 PhD student projects in progress, and three projects
exceeding the project time limits on 31 December 2009.
In 2010, 7 PhD student projects were completed, 10 new projects were started, and 1 project was left
unfinished. On 31 december 2010, 44 projects were still in progress. Three more projects exceeded the
project time limits and are therefor no longer mentioned in the 2010 summary of projects.
4.1
Status of projects
Completed projects
From 1 January - 31 December 2010, the following PhD students successfully defended their PhD theses:
1. Rudy Ligtvoet (Tilburg University)
2. Olga Lukociene (Tilburg University)
3. Gerben Moerman (VU University Amsterdam)
4. Joris Mulder (Utrecht University)
5. Rens Van de Schoot (Utrecht University)
6. Marieke Spreeuwenberg (Tilburg University)
7. Jorge Tendeiro (University of Groningen)
Projects in progress beyond project time limits
The projects of the following PhD students are still in progress, but have exceeded the project time limit:
1. Tina Glasner (Wagening University)
2. Marieke Polak (Leiden University)
3. Marthe Straatemeijer (University of Amsterdam
The above projects are no longer mentioned in the summary of projects
Projects left unfinished
The following student left the IOPS Graduate School before completing the project:
1. Deirdre Giesen (Utrecht University)
29
IOPS annual report 2010
New projects
From 1 January - 31 December 2010, the projects of the following 10 PhD students were accepted in the
IOPS Research School:
1. Mariska Barendse (University of Groningen)
2. Matthieu Brinkhuis (university of Amsterdam / Cito)
3. Britt Qiwei He (Twente University)
4. Gabriela Koppenol-Gonzalez Marin (Tilburg University)
5. Cor Ninaber (Leiden University)
6. Iris Smits (University of Groningen)
7. Daniel Van der Palm (Tilburg University)
8. Maaike Van Groen (Twente University / Cito)
9. Ruud Van Keulen (Tilburg University)
10. Gerko Vink (Tilburg University)
30
4 Students and projects
4.2
Summary of projects
A Bayesian approach for handling response bias and incomplete data
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Marianna Avetisyan
Department of Educational Measurement and Data Analysis
Faculty of Educational Science and Technology, Twente University
P.O. Box 217, 7500 AE Enschede
053 489 5583 / 3555 (secretary)
m.avetisyan@gw.utwente.nl
Prof. dr. C.A.W. Glas & dr. ir. J.-P. Fox
1 July 2008 - 1 July 2012
NWO (Netherlands Foundation of Scientific Research)
Summary
The collection of data through surveys on personal and sensitive issues may lead to answer refusals and
false responses, making inferences difficult. Respondents often have a tendency to agree rather than disagree (acquiescence) and a tendency to give socially desirable answers (social desirability). The randomized
response (RR) technique has been used to diminish the response bias. Attention will be focused on the
usefulness of the randomized response technique. Different settings will be explored, large-scale but also
small-scale survey data for binary and polytomous response data. Methodological developments will be
made to handle different settings and to test different real-data hypotheses.
Besides the problem of misreporting, respondents may not report an answer to one or more questions.
Missing data can also occur due to other causes like, interviewer errors (omitted questions, illegible recording of responses, etc.), and inadmissible multiple responses. In fact, it is not unusual for large data sets to
have missing data on a few items. The persons cannot be omitted from the analysis based on the fact that
they skipped a few questions since it will result in deletion of a substantial part of the data (these participants provide information on the answered items). In a Bayesian approach, the incomplete data problem
can be solved by repeatedly solving the complete data problem. In the setting of large-scale comparative
survey data, attention is focused on country-specific imputation methods and/or models for the missing
data mechanism.
31
IOPS annual report 2010
Expectancy effects on the analysis of behavioral research data
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Marjan Bakker
Department of Methodology, University of Amsterdam
Roetersstraat 15, 1018 WB Amsterdam
020 525 6763 / 6870 (secretary)
M.Bakker1@uva.nl
Prof. dr. H.L.J. Van der Maas , dr. J.M. Wicherts
1 March 2009 - 1 April 2013
NWO (Netherlands Foundation of Scientific Research)
Summary
Behavioral researchers normally try to avoid expectancy effects during data collection, but they perform
the statistical analysis of their study themselves. In this project we study whether researchers’ expectations
can bias their statistical results. We propose that researchers may suffer from confirmation bias which may
result in a failure to notice statistical errors that are in line with their hypotheses. Moreover, we
hypothesize that researchers may resort to alternative analyses when the planned analysis fails to support
their hypothesis. Expectancy effects on statistical outcomes will be studied by means of re-analyses and by
employing correlational, experimental, and meta-analytical methods.
32
4 Students and projects
Dimensionality assessmentof polytomous items (new project)
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Mariska Barendse
Heijmans Institute, Faculty of Behavioural and Social Sciences, University of
Groningen, Grote Rozenstraat 31, 9712 TG Groningen
050 363 9356 / 6340 (secretary)
m.t.barendse@rug.nl
Dr. M.E. Timmerman, dr. R.R. Meijer
1 September 2010 - 1 September 2014
NWO (Netherlands Foundation of Scientific Research)
Summary of project
Personality scales are popular instruments to measure individual’s traits. As important decisions on individuals are based on such measurements, scales of good quality are essential. Scale evaluation critically
depends on appropriately assessing the number of traits measured, that is, the dimensionality of the items.
For personality scales, which commonly consist of polytomous items, it is unclear which dimensionality
assessment method should be used. A comparative study, including various methods associated with factor
analysis and nonparametric item response theory, will be performed. The ultimate goal is to provide
proper, well-founded guidelines for the dimensionality assessment of polytomous items in personality
scales.
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IOPS annual report 2010
The theory and practice of item sampling (new project)
PhD student
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Supervisors
Project running from
Project financed by
Matthieu Brinkhuis
Psychometric Research Center (POC), CITO
P.O. Box 1034, 6801 MG Arnhem
26 352 1167 / 1124 (secretary)
matthieu.brinkhuis@cito.nl
prof. dr G.K.M. Maris
1 April 2008 - 1 April 2013
Cito / RCEC
Summary of project
In the seminal work of Lord and Novick, Statistical Theories of Mental Test Scores (1968), the idea of item
sampling is put forth. Though Johnson and Lord (1958) already introduced the idea a decade before, it
seems that it has not gained much popularity in neither literature nor applications since. One of the explanations for the lack of attention in this area might be the use of generalized symmetric means (gsm) (Lord
and Novick, p. 238), which are a highly complicated set of expressions limiting the usability of the whole
procedure.
However, responses gathered through randomly selected items hold several desirable properties for which
other procedures than the one suggested by Lord and Novick can be employed. Purpose of this proposal is
to develop and apply such alternative procedures, and thus to extend item sampling theory.
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4 Students and projects
Person-misfit in item response models explained by means of nonparametric and
multilevel logistic regression models
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Judith Conijn
Department of Methodology, Faculty of Social Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 2089 / 2544 (secretary)
j.conijn@uvt.nl
Prof. dr. K. Sijtsma, dr. M.A.L.M. Van Assen, dr. W.H.M. Emons
1 October 2007 - 1 October 2012
NWO (Netherlands Foundation of Scientific Research)
Summary
Performance on psychological tests and personality inventories may be unexpected. This may be due to
cheating or test anxiety (achievement testing), or response inconsistency or lack of traitedness (personality). Traditional person-fit measures are primitive in that they only flag unexpected performance but do
not provide explanatory information. Two recent approaches provide more explanatory information. One is
flexible (i.e., nonparametric) but only suggests an explanation. The other is not as flexible (i.e., parametric)
but explicitly uses auxiliary information in a multilevel framework. Both approaches are studied and
integrated so as to provide a better understanding of individual test performance.
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IOPS annual report 2010
Causal networks for psychological measurement
PhD student
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Supervisors
Project running from
Project financed by
Angélique Cramer
Department of Methodology, University of Amsterdam
Roetersstraat 15, 1018 WB Amsterdam
020 525 6876 / 6870 (secretary)
a.o.j.cramer@uva.nl
Dr. D. Borsboom, prof. dr. H.L.J. Van der Maas
1 March 2008 - 1 March 2012
NWO (Netherlands Foundation of Scientific Research)
Summary
Current psychometric models conceptualize psychological constructs as latent variables. Latent variables
function as the common cause of a number of observable ‘indicator’ variables; for instance, the latent
variable 'depression' is taken to be the common cause of a number of observable depression symptoms,
such as fatigue, depressed mood, and lack of sleep. Individual differences on the (aggregated) observable
indicators are then used to infer individual differences in the constructs measured. This is the logic of
construct validity theory, as it has been practiced in the past decades. For many important psychological
attributes, however, it is unlikely that this conceptualization is correct. For instance, the correlation
between sleep deprivation and fatigue is more likely to result from a direct effect (i.e., if you do not sleep,
you get tired) than from a common cause, as hypothesized in a latent variable model. In such situations, a
plausible hypothesis is that constructs like depression refer to causal networks that involve a set of
observables, rather than to the common cause of these observables. Indicator variables that are relevant to
a construct will, in such cases, be correlated; not, however, because they result from the same underlying
cause, but because they are part of the same causal system. Because this is fundamentally inconsistent
with existing psychometric theory, to accommodate situations in which constructs form causal networks, a
different methodological approach is needed. The present project aims to develop such an approach
through three subprojects: a) the development of new psychometric theory based on the assumption that
constructs are causal networks, b) the development of a methodological toolbox that allows for the
implementation of this theory in empirical research, and c) an application of the theory to diagnostic
systems used in clinical psychology.
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4 Students and projects
Linear logistic test models for rule-based item generation
PhD student
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Supervisors
Project running from
Project financed by
Hanneke Geerlings
Department of Educational Measurement and Data Analysis, Faculty of Educational
Science and Technology, Twente University, P.O. Box 217, 7500 AE Enschede
053 489 2506 / 3555 (secretary)
h.geerlings@gw.utwente.nl
Prof. dr. C.A.W. Glas, prof. dr. W. J. Van der Linden
1 September 2007 - 1 September 2011
Twente University
Summary
This project is embedded in a larger project called ‘Rule-based Item Generation of Algebra Word Problems
Based upon Linear Logistic Test Models for Item Cloning and Optimal Design’ that is funded by the
Deutsche Forschungsgemeinschaft (German Research Foundation). The project is a collaboration between
the Universities of Münster and Twente. In this project, techniques from cognitive analysis, item response
theory (IRT), hierarchical modeling, and optimal design theory are combined to develop procedures for
automated item generation and test assembly for the testing of basic mathematical competencies in early
secondary education, as can be assessed with algebra word problems. It will also be investigated how the
models and procedures should be optimized and generalized when they are applied in computerized
adaptive testing, testing for diagnosis, and large-scale educational assessments. The final goal is the
development of a software program which adaptively generates tailor-made items for algebra word
problems based on optimal design, linear-logistic test models, and models for test item cloning. The subproject presented here focuses on the statistical aspects of the project. Starting point is the classical
version of the linear-logistic test model (e.g., Fischer, 1995). This model will be extended through
incorporating random effects as well as interaction effects. The hierarchical model for item cloning will be
provided with a structure for the item parameters developed in other sub-projects. The parameters of the
model will be estimated in a Bayesian framework, by means of Markov Chain Monte Carlo (MCMC)
computation. If time allows, estimation in a frequentist framework (by means of Marginal Maximum
Likelihood, MML, estimation) can also be considered. The result will be used in the application of optimal
design techniques for automated test assembly from pools of item families. The selection criteria will be
based on the hyperparameters that describe the item families instead of the usual lower-level parameters
of the discrete items. Both information-based and Bayesian criteria for item selection will be studied.
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IOPS annual report 2010
Response burden and measurement error in business survey data collection
(student left IOPS before completion of the project)
PhD student
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Supervisors
Project running from
Project financed by
Deirdre Giesen
Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht
University, P.O. Box 80140, 3508 TC Utrecht
045 570 7388
igin@cbs.nl
Prof. dr. G.J.M. Snijkers, prof. dr. J.J. Hox, dr. F. van de Pol
1 January 2007 - 1 January 2010
CBS / Utrecht University
Summary
Traditionally, data collection methodology for household surveys has received more attention than
business surveys. Dillmann (2000) strikingly describes the implicit model for conducting government business surveys as a Cost-Compensation Model. This implicit model is guided by the overall aim to reduce costs
of data collection and the reliance on government authority in general and the mandatory nature of many
surveys in particular to compensate for the resulting lack of respondent friendliness.
However, times are changing and there is an increasing interest in data collection methodology for business
statistics (e.g. Sudman, Willimack, Nichols & Mesenbourg, 2000; Jones, 2003; Morrison, Stettler and Anderson, 2004; Bavdaz, 2006). Important factors at Statistics Netherlands in this respect are the overall goal to
measure and where necessary improve the quality of the statistical process and the aim to reduce both
actual and perceived response burden. In 2003, the Data Collection Expertise Program was established to
professionalize and coordinate the data collection for establishments at Statistics Netherlands (Snijkers,
Göttgens & Luppes, 2003).
The proposed research will be carried out by Deirdre Giesen, methodologist at the Division of Methodology
and Quality of Statistics Netherlands, as part of the research program of the Department of Methodology.
Goal of this research is to increase the quality of data collection for business surveys by reducing response
burden and by detecting, preventing and correcting non-sampling measurement error. An important result
of the research will be a proposal for a monitoring system for the data collection for business surveys at
Statistics Netherlands.
38
4 Students and projects
Computerized adaptive text-based testing in psychological and educational
measurement (new project)
PhD student
Address
Voice
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Supervisors
Project running from
Project financed by
Britt Qiwei He
OMD / Toegepaste Onderwijskunde, Twente University, P.O. Box 217, 7500 AE
Enschede
053 489 2829 / 3555 (secretary)
q.he@utwente.nl
Prof. dr. C.A.W. Glas, prof. dr. ir. Th. De Vries dr. ir. B.P. Veldkamp
1 February 2009 - 1 February 2013
Stichting Achmea Slachtofferhulp Samenleving
Summary of project
Computerized adaptive testing (CAT, Wainer et al., 1990, van der Linden & Glas, 2002, 2010 (in Press)) has
become increasingly popular during the past decade in both educational and psychological measurement.
The flexibility of CAT combined with the possibilities of internet-based testing seems profitable for many
operational testing programs (Bartram & Hambleton, 2006).
In CAT, the items are adapted to the level of the respondent, that is, the difficulty of the items is adapted to
the estimated level of the respondent. If the performance on previous items has been rather weak, an easy
item will be presented next, and if the performance on previous items has been rather strong, a more
difficult item will be selected for administration. The main advantage of this approach is that the test length
can be reduced considerably without loosing measurement precision. Besides, the respondents are
administered items at their specific ability level, which implies that they won’t get bored by to easy items
or frustrated by too difficult ones.
The measurement framework underlying CAT comes from Item Response Theory (IRT). One of the key
features of IRT is that both item and person parameters are distinguished in the measurement model. For
dichotomously scored items, the probability of a correct or positive response depends on person parameters such as the ability level of the person and on item parameters such as the difficulty-, discriminationand pseudo-guessing parameter. For a thorough introduction to IRT, one is referred to Hambleton and
Swaminathan (1985) or Embretson and Reise (1991).
In this PhD project, the focus is on open answer questions where more complicated automated scoring
algorithms have to be developed. Applications are either within the context of psychological or educational
measurement.
The technology of CAT has been developed for multiple-choice items in the cognitive domain that are
dichotomously or polytomously scored. For these items, both the correct and the incorrect answers are
precisely defined and automated scoring can be implemented on the fly. For other item types, application
of CAT is less straightforward.
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IOPS annual report 2010
For example for open-answer questions, automated scoring rules can be much more complicated. Further,
CAT is more and more applied outside the traditional cognitive domain. Initially, the present project will
focus on the assessment of post traumatic stress disorder (PTSD).
40
4 Students and projects
The potential of Item Response Theory to predict social acceptance of new technologies: Bionanotechnology
PhD student
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Supervisors
Project running from
Project financed by
Tamara Hendrick
Marketing and Consumer Behaviour Group, room 5016, Wageningen University
P.O.Box 8130, 6700 EW Wageningen
0317 48 3636 / 3385 (secretary)
tamara.hendrick@wur.nl
Prof. Dr. Lynn J. Frewer, Dr. Hilde Tobi, Dr. Arnout R. H. Fischer
1 June 2008 - 1 June 2012
Wageningen University (IP/OP)
Summary
A recent technological development of potential benefit to the agri-food sector in particular, and society
more generally, is nanobiotechnology. However, for this technology to reach its full, it must first be
accepted by society. Public attitudes towards nanobiotechnology are likely to be effective predictors of
acceptance of both technology and its applications, are therefore relevant to its strategic development and
commercialisation.
However, measuring current attitudes towards nanobiotechnology proves difficult, because existing attitudes tend to be uncrystalised. In addition, existing methodologies are unsuccessful at predicting these
attitudes where these circumstances apply. A statistical model that can predict individual attitudes towards
different applications of nanobiotechnology is the item response theory (IRT).
An attractive property of IRT is that it is invariant, making the model independent of group responses and
test items. Application of IRT will enable attitudinal assessment to occur for different groups of respondents or acrossdifferent bionanotechnology applications.The objective of the research is to improve
predictions of social acceptance of emerging technologies and their applications by developing a valid and
reliable methodological approach to instrument development. An important research activity relates to the
measurement of attitudes towards nanobiotechnology and its applications.
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IOPS annual report 2010
Mathematical proficiency in primary education: Cognitive processes and
predictability
PhD student
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Supervisors
Project running from
Project financed by
Marian Hickendorff
Psychometrics and Research Methodology, Department of Psychology, Faculty of
Social and Behavioral Sciences, Leiden University, P.O. Box 9555, 2300 RB Leiden
071 527 3765 / 3761 (secretary)
hickendorff@fsw.leidenuniv.nl
Prof. dr. W.J. Heiser, dr. C.M. Van Putten, dr. N.D. Verhelst
1 January 2006 - 1 January 2011
Leiden University / Cito Arnhem
Summary
General aim of this project is to systematically describe and analyze mathematical proficiency in primary
education. Reform in mathematics education and changes in mathematical achievement give rise to the
need for this research. The present study aims at going beyond analysis of pure achievement, in the
following way: by applying advanced data analysis techniques which have an opportunity to include
predictor variables and by extending the type of data analyzed with information on the cognitive processes
involved in solving mathematical problems. So, several advanced data analyses will be conducted in which
the effects of predictor variables are included, on data with information on cognitive processes in addition
to correct/incorrect scoring to get robust substantive conclusions. The research objectives lie in two
domains. Primary objectives are in the domain of mathematics education, where exploratory analyses
followed by carefully set up data collections should lead to a deeper understanding of the processes
involved in and the predictability of mathematical proficiency. In the domain of psychometrics, three data
analysis techniques aimed at exploration of the data will be compared, and the validity of the construct
mathematical proficiency. will be explored by focusing on the response processes.
42
4 Students and projects
Bias in the measurement of child attributes in educational research: Measurement
bias in multilevel data
PhD student
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Supervisor
Project running from
Project financed by
Suzanne Jak
Department of Pedagogical & Educational Sciences, University of Amsterdam
Nieuwe Prinsengracht 130, 1018 VZ Amsterdam
020 525 1261 / 1201 (secretary)
s.jak@uva.nl
Dr. F.J. Oort
1 January 2009- 1 January 2014
University of Amsterdam
Summary
Background
The measurement of child attributes brings about problems because informants (e.g., the children themselves, their parents, their teachers, etc.) may have different frames of reference when answering test or
questionnaire items. Such different frames of reference may result in measurement bias, so that observed
differences and changes in test scores do not reflect true differences and changes in child attributes.
Measurement bias thus complicates all research into child attributes (e.g., evaluation of intervention
effects, sex differences, cultural differences, relationships with explanatory variables).
Objectives
We will extend existing structural equation modelling (SEM) procedures for the detection of measurement
bias with procedures for bias detection in multilevel data, continuous and discrete.
We will investigate the feasibility of these new procedures, by applying them in secondary analyses of
educational data, investigating the impact of measurement bias on the results of testing substantive hypotheses in educational research, and investigating different ways to account for apparent measurement bias.
Method
We will first investigate measurement bias in existing data sets of our department by means of secondary
analyses. When we find measurement bias, we will account for this bias, and investigate whether the test
results of the original hypotheses are different from the test results that are obtained when measurement
bias is accounted for. Dependent on our findings, we may modify the SEM procedures, and further
investigate the latent variable modelling procedures with simulated data, e.g., to investigate power, effect
size indices, and the impact of measurement bias. This approach will be used with various sets of multilevel
data, and various sets of discrete data.
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IOPS annual report 2010
Relevance
We will obtain additional knowledge of:
(1) the psychometric properties of several measurement instruments that are commonly applied in
educational research,
(2) the extent of measurement bias in educational research,
(3) the impact of possible measurement bias on substantive conclusions,
(4) the robustness of educational research to possible measurement bias. Moreover, the research project
is psychometrically relevant because it extends and further develops procedures for testing
measurement bias in multilevel data, continuous and discrete. Methods to detect measurement bias
and to account for measurement bias will result in stronger substantive conclusions.
44
4 Students and projects
The use of item response theory for scaling in educational surveys
PhD student
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Supervisors
Project running from
Project financed by
Khurrem Jehangir
Department of Educational Measurement and Data Analysis, Faculty of Educational
Science and Technology, Twente University, P.O. Box 217, 7500 AE Enschede
053 489 3864 / 3555 (secretary)
k.jehangir@gw.utwente.nl
Prof. dr. C.A.W. Glas, dr. A.A. Béguin
1 May 2006 - 1 September 2011
Twente University
Summary
This project focuses on the application of item response theory (IRT) in the context of large-scale
international educational surveys, such as PISA, TIMSS, CIVICS and PEARLS. Although IRT methodology has
been widely used in educational applications such as test construction, norming of examinations, detection
of item bias, and computerized adaptive testing, large scale education surveys present a number of specific
problems. A number of these problems are addressed in the present proposal.
The first problem relates to the detection of cultural bias over countries. Statistical tests to detect item bias
are available, but the sheer numbers of students (over 10.000) and countries (between 30 and 70) present
feasibility problems related to the power of the tests and the presentation of the tests results, which has to
be concise and meaningful. Therefore, test statistics will probably need to be redefined and functions for
these statistic need to be defined that give information with respect to the seriousness of model violations
in relation to the inferences that need to be made.
The second problem relates to modeling of item bias. One of the possibilities in this respect that will be
investigated is modeling item bias by adding country-specific item parameters or item parameters which
are random over the countries. A related problem is the definition of test statistics which support the
appropriateness the bias model.
The third problem relates to the combination of the results of IRT measurement models with multilevel
structural models that relate cognitive outcomes with background variables. Several procedures are
available (concurrent and two-step procedures, maximum likelihood, Bayesian procedures and plausible
value imputation). A study will be made of the relative merits and disadvantages of these methods. The
fourth problem relates to linking surveys, predominantly over cycles within a survey, but possibly also
between surveys. The possibility of linking arises because a survey as PISA retained a number of cognitive
items and background questions over the cycles (2000, 2003, 2006 and 2009). The possibility of linking over
surveys may be supported by such occasions as common items and questions or a common framework. In
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IOPS annual report 2010
the latter case, a dedicated linking design may be called for. The psychometric problems related to these
forms of linking, both pertaining to the measurement model and the structural model will be investigated.
The supervisors of this research project are involved in a consortium led by Cito to implement Core B
(background questionnaires) of the fourth cycle of the PISA by OECD. The proposed methods will be
evaluated using examples of the various PISA cycles. However, the method will also be evaluated using data
from the TIMSS project, and using data from national assessments as PPON and NAEP.
46
4 Students and projects
Improving statistical power in studies on event occurrence by using an optimal
design
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Kasia Jozwiak
Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht
University, P.O. Box 80140, 3508 TC Utrecht
030 253 7983 / 4438 (secretary)
k.jozwiak@uu.nl
Dr. ir. M. Moerbeek, prof. P.G.M. Van der Heijden
1 January 2009 - 1 January 2013
NWO (Netherlands Foundation of Scientific Research)
Summary
The main research question in studies on event occurrence is whether and when subjects experience a
particular event, such as the onset of daily smoking or the shift to adulthood. The experience of such an
event and its timing can be related to explanatory variables such as gender, socio-economic status,
educational level, and, in the case of an experiment, treatment condition. Such a variable’s effect should be
identifiable with sufficient probability, so the power of a study on event occurrence should be controlled in
the design phase. In studies on event occurrence subjects may be monitored continuously, or be measured
at intervals. Interval measurement is often used in the behavioural sciences but sample size formulae for
such trials are not readily available. The proposed research aims to remedy this deficiency by providing
guidelines for the indices governing the number of subjects, the number of measurements per subject, the
placement of the measurement points in time and the duration of the study. Where possible, mathematical
formulae that relate sample size and duration to statistical power will be derived analytically.
Otherwise, the effect of these design factors on statistical power will be studied on the basis of simulation
studies taking into account realistic conditions such as drop-out rates and the varying costs per treatment
condition.
A study that is not carefully designed is a waste of resources. Therefore, ethical review committees and
organizations funding scientific research frequently require research proposals to include power calculations. The proposed research will provide guidelines for efficient study-designs for use in event occurrence
studies – ensuring that the financial cost and the number of subjects are minimized and sufficient power is
guaranteed. From a scientific point of view this proposed research project is fundamental since it will
enable future researchers to plan their research more efficiently.
Keywords: statistical power, cost-efficient designs, survival analysis, hypothesis testing.
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IOPS annual report 2010
Testing the mutualism model of general intelligence
PhD student
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Supervisor
Project running from
Project financed by
Kees Jan Kan
Department of Methodology, University of Amsterdam
Roetersstraat 15, 1018 WB Amsterdam
020 525 6876 / 6870 (secretary)
k.j.kan@uva.nl
Prof. dr. H.L.J. Van der Maas, dr. C.V. Dolan
1 April 2007 - 1 April 2011
University of Amsterdam
Summary
Van der Maas, Dolan, Grasman, Wicherts, Huizenga & Raijmakers (2006) proposed a new theory of general
intelligence based on the idea of mutualistic interactions during development between the cognitive
processes underlying intelligence. They showed that such interactions lead to a positive manifold of
correlations between scores on cognitive tasks. This theory is an important alternative for the standard g
theory (Jensen, 1998), which conceptualized g as a single latent dimension. The aim of this project is to
further investigate the mutualism model. Topics are: model extension, model equivalence, evidence from
experimental data, and evidence from longitudinal correlational data.
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4 Students and projects
Question format and response style behaviour in attitude research
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Natalia Kieruj
Department of Methodology, Faculty of Social Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 3527 / 2544 (secretary)
n.d.kieruj@uvt.nl
Prof. dr. J.K. Vermunt, dr. G.B.D. Moors
1 September 2007 - 1 May 2011
NWO (Netherlands Foundation of Scientific Research)
Summary
Attitude questions differ in format, e.g. differences in numbering and labelling of response categories. It
has been argued that the validity and reliability of attitudes is affected by the choice of question format. At
the same time, it is acknowledged that response style behaviour can bias the measurement of attitudes as
well as bias the estimates of the effect of covariates. This research project links these two issues by
focusing on the impact of question format on the likelihood of response bias, i.e. acquiescence and
extreme response style, in attitude research.
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IOPS annual report 2010
Statistical models for reductive theories
PhD student
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Supervisors
Project running from
Project financed by
Rogier Kievit
Department of Developmental Psychology, Faculty of Psychology, University of
Amsterdam, Roetersstraat 15, 1018 WB Amsterdam
020 525 6688 / 6830 (secretary)
r.a.kievit@uva.nl
dr. D. Borsboom, dr. L.J. Waldorp, dr. J.W. Romeijn, prof dr. H.L.J. Van der Maas
1 August 2008 - 1 August 2012
University of Amsterdam
Summary
This project reformulates the reduction problem as measurement problem, by focusing on the question
how we should combine physical and psychological indicators in a single measurement structure. In the
first subproject, different positions that have been articulated in the philosophy of mind, such as identity
theory and supervenience, are translated into different psychometric models. In the second subproject,
these models are applied to existing datasets involving a) the relation between IQ and physical properties
of the brain (e.g., brain volume), b) the relation between EEG measures of speed of processing and IQ,and
c) the relation between anatomical differences in the brain and different kinds of synesthetic experience. In
the third subproject, the prospects for a reductive explanation of inter-individual differences on the basis of
intra-individual processes is evaluated according to theoretical insights taken from the philosophical
literature on reduction.
50
4 Students and projects
Unbiased measurement of health-related quality-of-life
PhD student
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Supervisors
Project running from
Project financed by
Bellinda King Kallimanis
Department of Medical Psychology, University Medical Center Amsterdam (AMC)
Meibergdreef 15, 1105 AZ Amsterdam
020 566 7112 / 4661 (secretary)
b.l.kallimanis-king@amc.uva.nl
Dr. F.J. Oort, prof. dr. M.A.G. Sprangers
12 March 2008 - 12 March 2012
Academic Medical Centre, University of Amsterdam
Summary
Problem and objective
Health-related quality-of-life (HRQL) is generally measured through self-report. Selfassessment brings
about the problem that patients may have different frames of reference when answering HRQL items. As a
result, the measurement of HRQL may be biased. That is, observed differences in HRQL scores may reflect
something else than true differences in HRQL. Measurement bias may not only be caused by differences in
individual and environmental characteristics (e.g., gender, age, education, ethnicity, mother tongue), but
also by differences in treatment and other clinical variables (e.g., diagnosis, disease severity). Therefore,
even when patients are randomised, treatment effects on HRQL are biased when treated patients have
another frame of reference than control patients. In the proposed research, the objectives are to identify
predominant sources of bias in the measurement of HRQL, to account for these biases, and to determine
the clinical significance of true effects on unbiased HRQL.
Method
Structural equation modelling with latent variables (LVM) provides a way to detect measurement bias, to
account for apparent bias, and to measure true (i.e., unbiased) effects on HRQL. In the proposed research,
LVM will be used in secondary analyses of existing data sets from randomised and non-randomised trials in
clinical and psychosocial medicine. We will examine a range of clinical, individual, and environmental
sources of bias in HRQL outcomes. Several suitable data sets are available for secondary analysis. The
clinical significance of both measurement bias and true effects in HRQL will be evaluated with a
generalisation of the “number needed to treat” and some other effect size indices used in medical and
social science research.
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IOPS annual report 2010
Possible results
We will obtain insight into the size of measurement bias and its impact on observed differences, changes
and effects in HRQL. With that, we will also obtain insight into the true effects of clinical, individual, and
environmental variables on (unbiased) HRQL, and into the clinical significance of these true effects.
Knowledge of true effects in HRQL will facilitate treatment decisions and patient care, and thus further
evidence-based medicine.
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4 Students and projects
The influence of strategy use on working memory task performance (new project)
PhD student
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Supervisors
Project running from
Project financed by
Gabriela Koppenol-Gonzalez Marin
MTO, Tilburg School of Social and Behavioral Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 4030 / 2544 (secretary)
g.v.gonzalezmarin@uvt.n
prof. dr J.K. Vermunt, dr. S. Bouwmeester
15 March 2009 - 15 March 2013
Tilburg University
Summary
There are some robust effects on WM that are replicated in different studies over the years, like the visual
similarity effect and the phonological similarity effect (e.g., Hitch et al., 1989; Poirier et al., 2007). The
nature of these effects has been investigated, but research in which group means are compared show
inconsistent results. Other researchers have focused more on the methodology and individual differences
in WM research (e.g., Logie et al, 1996; Della Sala & Logie, 1997; Engle, 1999). These studies have shown
that there are different influences on performance besides the aforementioned effects, like task demands
and strategy use. Because this focus seems to lead to useful information about the cognitive processes
involved in working memory, there is a need for further refinement of the methodology. The aim of this
project is to address this issue. First, we want to investigate the development of WM and test the
hypothesis that younger children process information mostly visually, whereas older children process
information mostly verbally. Second, we want to further investigate this question by distinguishing the
different cognitive processes that underlie the different strategies. Third, we want to explore different
measurement tools that enable us to investigate the influence of strategy use and task demands on
performance in order to better understand the model of working memory of Baddeley and Hitch and its
generalization. Finally, in addressing these aims, we will apply a latent variable approach.
53
IOPS annual report 2010
Robustness issues for cluster randomised trials
PhD student
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Supervisors
Project running from
Project financed by
Elly Korendijk
Department of Methodology and Statistics, Faculty of Social and Behavioural
Sciences, Utrecht University, P.O. Box 80140, 3508 TC Utrecht
030 253 7983 / 4438 (secretary)
e.j.h.korendijk@uu.nl
Prof. dr. P.G.M. Van der Heijden, dr. ir. M. Moerbeek
1 September 2005 - 1 September 2010
NWO (Netherlands Foundation of Scientific Research)
Summary
Cluster randomised trials randomise complete groups to treatment conditions. The estimates of the model
parameters and their standard errors are only correct if the chosen statistical regression model includes all
necessary fixed and random effects, and if the model assumptions are satisfied. Furthermore, optimal
designs for cluster randomised trials depend on the values of certain model parameters, of which the true
values must be specified in the design stage. This study researches two questions: What is the robustness
of optimal designs and estimation methods? What should be done to correct for an incorrect model or an
incorrect guess of the model parameters?
54
4 Students and projects
Minimal requirements of the reliability of tests and questionnaires
PhD student
Address
Voice
Email
Supervisors
Project running from
Project financed by
Peter Kruyen
Department of Methodology, Faculty of Social Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 3527 / 2544 (secretary)
p.m.kruyen@uvt.nl
Prof. dr. K. Sijtsma, dr. W.H.M. Emons
15 November 2008 - 15 November 2012
NWO (Netherlands Foundation of Scientific Research)
Summary
A test’s reliability often is the basis for advise to test constructors, researchers and test users on which test
to use for accurately classifying individuals in diagnostic categories. However, the classical reliability
coefficient does not provide information that is adequate for this purpose. This study investigates how
individual classification accuracy depends on properties of the test and its items, the population studied,
and the decision-making problem. Its output will be tables that give the minimum quality requirements for
tests and their constituent items, given a known population distribution and a well-defined classification
problem.
55
IOPS annual report 2010
Chained equations
PhD student
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Supervisors
Project running from
Project financed by
Rebecca Kuiper
Department of Methodology and Statistics, Faculty of Social and Behavioural
Sciences, Utrecht University, P.O. Box 80140, 3508 TC Utrecht
030 253 1571 / 4438 (secretary)
r.m.kuiper@uu.nl
Prof. dr. H.J.A. Hoijtink
1 May 2007 - 1 May 2012
NWO (Netherlands Foundation of Scientific Research)
Part of Vici project by H.J.A. Hoijtink “Learning more from empirical data using prior
knowledge”
Summary
Theories often have multiple implications that have to be evaluated. Multiple hypotheses addressing
different variables are not easily summarized in one statistical model, because often it is too complicated to
account for the dependencies between the variables. Multiple hypotheses are usually evaluated separately
which increases the probability of errors of the first kind and/or reduces the power. See, for example,
Toothaker (1993), Benjamini and Hochberg (1995) and Maxwell (2004) for a discussion of these matters. In
this project chained equations (van Buuren, Boshuizen, Knook, 1999; Raghunathan, Lepkowski, Van
Hoewyk, and Solenberger, 2001; Buuren, Brand, Groothuis-Oudshoorn and Rubin, to appear) will be used
to build statistical models for multiple hypotheses addressing the same or different data sets. Chained
equations have thus far been used for multiple imputation of missing values. Here they will be used to build
one statistical model for the evaluation of multiple hypotheses.
56
4 Students and projects
Models and methods for invariant item ordering (project completed)
PhD student
Affiliation
Project financed by
Project running from
Date of defence
Title of thesis
Promotores
Rudy Ligtvoet
Department of Methodology, Faculty of Social Sciences, Tilburg University
Tilburg Unversity
1 January 2006 - 1 January 2010
16 April 2010
Essays on invariant item ordering
Prof. dr. K. Sijtsma, prof. dr. J.K. Vermunt, dr. A. Van der Ark
Summary
Items in a test or a questionnaire have an invariant item ordening (IIO) if the items have an identical
ordering according to difficulty or atractiveness for every individual in the population of interest, with the
exeption of possible ties. Several applications of test results require an IIO. Examples are the investigation
of developmental theories, differential item functioning, aberrant response patterns, and the use of
starting and stopping rules in administration. This Ph.D project proposal contains four parts, each investigating different aspects of the ordering of polytomous items in a test or questionnaire. The first project aims
at the development of an item response theory model, which implies an invariant item ordering of
polytomous items, which implies an invariant ordering. The psychometric properties of these models will
be investigated. Second, methods for testing the invariant ordering of polytomous items, which have not
yet been investigated thoroughly, are further developed and compared. Third, the use of latent class
analysis as a tool for estimating item orderings is explored. Fourth, the newly developed models and
methods are applied in the real data.
57
IOPS annual report 2010
Tailoring to the MAX: Using new IC technology to increase data quality and
efficiency in panel surveys
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Peter Lugtig
Department of Methodology and Statistics, Faculty of Social and Behavioural
Sciences, Utrecht University, P.O. Box 80140, 3508 TC Utrecht
030 253 4764 / 4438 (secretary)
p.lugtig@uu.nl
Prof. dr. J.J. Hox, dr. G.J.L.M. Lensvelt-Mulders
1 September 2007 - 1 September 2012
Utrecht University
Summary
Panel studies hold the promise of providing reliable and valid data on change over time. This dissertation
project investigates measurement error in panel data with the aim to improve the quality of future data
collection and to enhance the scientific knowledge of the question-answer process. The possibilities of
dependent interviewing techniques (DI) and the analysis of attrition patterns to improve data quality and
survey efficiency will be evaluated. We compare three alternative approaches to dependent interviewing
(proactive, reactive and optional) with traditional interviewing to study the effects of the different designs
on measurement error. To do so we propose to conduct a 4×2×2 experimental design. Three main effects
will be studied:
1) The effects of four different techniques for dependent interviewing on measurement error and
stability of traits over time,
2) the effects of anchoring as a result of DI, and
3) the effects of DI on different kind of questions i.e. facts and attitudes.
All interaction effects will be studied as well. Attrition patterns will be studied and used to improve the
imputation of missing data and in doing so improve the estimation of substantive variables. Because the
methodological problems studied in this project stem from respondent’s behaviour this project will be a
joint work of the Departments of Methods and Statistics and Psychology of Utrecht University. Five
hundred first year students will take part in a longitudinal survey on students’ motivation, satisfaction, and
grades, related to the development of their academic literacy during their bachelor years.
58
4 Students and projects
Performance of latent class analysis based random-coefficient models (project
completed)
PhD student
Affiliation
Project financed by
Project running from
Date of defence:
Title of thesis
Promotor
Olga Lukociene
Department of Methodology, Faculty of Social Sciences, Tilburg University
NWO (Netherlands Foundation of Scientific Research)
1 September 2004 - 1 September 2008
26 March 2010
Latent class models for categorical data with a multilevel structure
Prof. dr. J.K. Vermunt
Summary
The two basic assumptions underlying standard linear random-effects models - normal errors and normal
random effects - may be unrealistic in social science research. Outcome variables of interest are very often
categorical variables, which makes it necessary to use non-linear mixed models. Also the distributional
assumptions about random effects are not realistic in most applications. Latent class regression analysis
provides an alternative nonparametric approach that relaxes this assumption and that makes it straightforward to deal with categorical outcome variables. The objective of this project is to provide a systematic
comparison between parametric and nonparametric random-coefficients models.
59
IOPS annual report 2010
Simulator-based automatic assessment of driving performance
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Maarten Marsman
Central Institute for Educational Measurement (CITO)
Amsterdamseweg 13, 6814CM Arnhem
026 352 1003 / 1124 (secretary)
maarten.marsman@cito.nl
Prof. dr. C.A.W. Glas, prof. dr. K. Brookhuis, dr. M.J.H. Van Onna
1 January 2009 - 1 january 2014
Cito Arnhem and RCEC (Twente University)
Summary
The purpose of this PhD project is to design a reliable and valid automatic performance scoring system for a
simulator based test for driving.
In order to design a simulator test, apart from optimizing the technical or virtual presentation of the
scenario’s in the simulator, several statistical and methodological problems have to be tackled. First,
because performance in the simulator cannot be automatically scored yet, assessors have to be used to
obtain evaluation of pupil driver behaviour. A cognitive model is developed at TNO that learns the relation
between ratings of assessors and registered objective performance measures by the simulator. Since the
quality of the cognitive model is dependent on the quality of the information provided by assessors, a
sound IRT-based measurement model for the assessors’ data has to be developed to feed the cognitive
model with optimal information.
The output of the cognitive model will be used to select objective measures which are good predictors of
the judgements of the assessors. Then a compound IRT model will be designed where one element is the
IRT-based measurement model for the assessor judgements and the other an IRT model for assessment
based on the selected predictors.
When the test has been designed and the models have been developed and validated, two projects remain.
First, a cross-sectional study will be performed to create norm distributions for groups defined as beginning
pupil drivers, advanced pupil drivers, license candidates, drivers one year post-licences, and very
experienced drivers. Second, the assessors’ and simulator assessment scores will be correlated with
additional measurements of supposedly related cognitive processes involved in driving, in particular in-car
performance assessments, self-evaluation of driving competence and the Cito Drive computer based tests
of responsible driving.
60
4 Students and projects
Optimal designs for fMRI experiments
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Bärbel Maus
Group Methodology & Statistics, Faculty of Health, Medicine and Life Sciences,
Maastricht University, P.O. Box 616, NL-6200 MD Maastricht
043 388 2905 / 2395 (secretary)
baerbel.maus@@maastrichtuniversity.nl
Prof. dr. M.P.F. Berger, prof. dr. R. Goebel, prof. dr. L.M.G. Curfs, dr. G.J.P. Van
Breukelen
1 October 2006 - 1 October 2010
NWO (Netherlands Foundation of Scientific Research)
Summary
Cognitive processes can be studied with functional magnetic resonance imaging (fMRI) experiments.
Different within subject and between subject designs exist with their own advantages and disadvantages.
This research project aims at finding optimal designs for fMRI experiments that have maximal efficiency
and maximal power for finding real effects. By means of results from the statistical theory of optimal
designs for generalized linear mixed effects models, including both random and fixed parameters together
with (auto)correlated errors, the problem of finding optimal designs can be formulated as a nonlinear
optimisation problem. The optimal designs will be empirically evaluated with real fMRI data.
61
IOPS annual report 2010
Interview-strategies in open interviews (project completed beyond project’s time
limits)
PhD student
Affiliation
Project financed by
Project running from
Date of defence
Title of thesis
Promotores
Gerben Moerman
Department of Social Research Methodology, Faculty of Social Sciences, VU University Amsterdam
VU University Amsterdam
15 November 2001 - 15 November 2006 (0.8 fte)
16 April 2010
Probing behaviour in open interviews, 168 pp.
Prof. dr. J. Van der Zouwen & dr. H. Van den Berg
Summary
This research is a comparative research in interview-strategies in open interviews, which are being used in
pilot studies for preparation and development of new questionnaires, or assessment and correction of used
questionnaires in standardised survey research. The research is focussed on the effects of distinguished
interview-strategies for open interviews on the quality of acquired information. To compare interviewstrategies the research question concentrates on one dimension of interview-strategies: ways of
respondent's treatment by the interviewer in question and answer sequences. Two topics are considered,
to determine whether the effects of interviewer-strategies are topic-dependent: Ethnic categorisation, as a
controversial topic, and categorisation of primal relations.
What is the influence of ways of respondent's treatment by the interviewer in open interviews in question
and answer sequences on the quality of the acquired information about ethnic categorisation and
categorisation of primal relations?
62
4 Students and projects
Statistical modeling of (cognitive) ability differentiation
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Dylan Molenaar
Department of Developmental Psychology, Faculty of Psychology, University of
Amsterdam, Roetersstraat 15, 1018 WB Amsterdam
020 525 6584 / 6830 (secretary)
d.molenaar@uva.nl
Prof. dr. H.L.J. Van der Maas, dr. C.V. Dolan
1 September 2007 - 1 September 2011
NWO (Netherlands Foundation of Scientific Research), Top Talent Grant
Summary
No suitable procedures are yet available to investigate ability differentiation, although this phenomenon
has important implications for the measurement of cognitive abilities. The aim of the present project is to
develop, test, and apply suitable models to investigate ability differentiation.
63
IOPS annual report 2010
Bias and equivalence in cross-cultural survey research: An analysis of instrument
comparability in the SPVA survey
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Meike Morren
Department of Methodology, Faculty of Social Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 8046 / 2544 (secretary)
m.morren@uvt.nl
Prof. dr. J.K. Vermunt, dr. J.P.T.M. Gelissen
1 February 2007 - 1 February 2011
NWO (Netherlands Foundation of Scientific Research)
Summary
Sociological cross-cultural survey studies often ignore the problem of cross-cultural equivalence, thereby
tacitly assuming that concepts and terminology are being equally and equivalently evaluated by members
in all respondent groups. This project sets out to invest igate to what extent comparabilit y holds for inst
ruments included in the publicly and scientifically important Dutch survey "Sociale Positie en Voorzieningengebruik van Allochtonen (SPVA)" . The research uses a mixed-methods research design. The survey is
first analyzed with statistical methods for bias detection. Focus groups act as a follow-up that provide
information about respondents’ underlying thought processes and assists in interpreting the statistical
results.
64
4 Students and projects
Inequality constrainted models for the multivariate normal mean: A Bayesian
approach (project completed)
PhD student
Affiliation
Project financed by
Project running from
Date of defence
Title of thesis
Promotores
Joris Mulder
Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht
University
NWO (Netherlands Foundation of Scientific Research)
Part of Vici project by H.J.A. Hoijtink “Learning more from empirical data using prior
knowledge”
1 September 2006 -1 September 2010
3 December 2010
Bayesian model selection for constrained multivariate normal linear models
Prof. dr. H.J.A. Hoijtink, dr. ir. G.J.A. Fox, Dr. I.G. Klugkist
Summary
Instead of focussing on null hypothesis testing, a researcher is often interested in the alternative situation
in which the null hypothesis does not hold. The researcher may have several competing theories that have
to be tested with the collected data. Competing theories on the means can be, for instance, that the means
are increasing from time point 1 to 4 or that the means at time point 1 and 2 are smaller than the means at
time point 3 and 4. The aim of this research project is to address this issue for models for the multivariate
normal mean using a Bayesian approach. Furthermore, issues that a researcher encounters such as missing
data, adjustment of a constrained model, and model averaging are addressed.
65
IOPS annual report 2010
Prediction of disease classes using resting rate state neuroimaging data
(new project)
PhD student
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Supervisors
Project running from
Project financed by
Cor Ninaber
Methodology and Statistics Unit, Department of Psychology, Faculty of Social
and Behavioral Sciences, Leiden University, P.O. Box 9555, 2300 RB Leiden
071 527 1989 / 3761 (secr.)
ninaberc@fsw.leidenuniv.nl
Dr M. De Rooij, prof. dr W.J. Heiser, prof. dr S.A.R.B. Rombouts
1 March 2010 – 1 March 2014
NWO (Netherlands Foundation of Scientific Research)
Summary
Resting state functional magnetic resonance imaging (RS-fMRI) has become a very popular technique to
study functional connectivity in the brain. It appears that the brain is very active even in the absence of
explicit input or output behavior. The networks obtained in rest, resemble networks that are typically
observed activated during cognitive, sensory or motor tasks and this therefore providing insight into the
intrinsic functional architecture of the brain.
Furthermore, functional connectivity measures have improved our understanding of variability of behavior
and associated brain activity. In addition, RS-fMRI has provided insight in alterations in brain activity between healthy, dementia, depression, ADHD, autism, schizophrenia, Parkinson’s disease, and MS subjects.
Most investigations are limited to studying whether brain signals differ between patient and control
groups. These studies provide important new insights about average (group mean) functional brain connectivity changes in diseases. However, to understand to what extent this innovative technique can be applied
for (early) diagnostics en treatment predictions, it is of great interest to study whether we can classify a
subject based on his/her RS-fMRI scans. Meaning we are able to see whether RS-fMRI scans of a single
subject allow us to determine whether a subject has for instance Alzheimer’s disease, a depression, etc, or
is healthy.
Suppose there are brain scans of n subjects, which are known to come from different disease classes. The
question is whether we can distinguish these groups on the basis of the brain scans, and whether we can
accurately predict the status of a single subject based on earlier obtained rules. This is a typical
classification question, normally solved using discriminant analysis or some form of logistic regression, but
in this case the number of variables is very large, i.e. the measurements on each of the voxels at each of the
time points (volumes)
This project’s aim is to develop techniques for building highly sophisticated classifion rules, which can be
used as a multiclass prediction tool for RS-fMRI scans.
66
4 Students and projects
Prediction of the quality of survey questions in cross cultural research
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Daniel Oberski
Department of Methodology, Faculty of Social Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 8046 / 2544 (secretary)
daniel.oberski@gmail.com
Prof. dr. J.A.P. Hagenaars, prof. dr. W.E. Saris, (ESADE, Universidad Ramon Llull,
Barcelona, Spain)
1 October 2006 - 1 October 2011
ESADE, Universidad Ramon Llull, Barcelona, Spain /Tilburg University
Summary
Without correction for measurement error, comparative survey research is not possible. During the last 15
years research was done to develop a scientific approach to predict and improve the quality of survey
questions. This led to the development of an application programme, SQP, that predicts the quality of
survey questions for 3 languages (Dutch, English and German). Now in the European Social Survey (ESS), 20
multitrait-multimethod experiments have been done in more than 20 languages. This huge database offers
a tremendous and incomparable opportunity to further investigate the quality of survey questions and the
development of the SQP programme for 20 languages. With this programme measurement errors in survey
questions can then be investigated and predicted which is essential for comparative survey research in
Europe.
67
IOPS annual report 2010
Inequality constrainted models for the multivariate normal mean: A Bayesian
approach
PhD student
Address
Voice
E-mail
Supervisor
Project running from
Project financed by
Carel Peeters
Methodology and Statistics, Faculty of Social and Behavioural Sciences
Utrecht University, P.O. Box 80140, 3508 TC Utrecht
030 253 1227 / 4438 (secretary)
c.f.w.peeters@uu.nl
Prof. dr. H.J.A. Hoijtink
1 February 2007 -1 September 2011
NWO (Netherlands Foundation of Scientific Research)
Part of Vici project by H.J.A. Hoijtink “Learning more from empirical data using prior
knowledge”
Summary
Researchers often have competing theories that can be translated into inequality constrained models. Such
theoretical models cannot be addressed with standard null-hypothesis testing. In this project inequality
constrained Bayesian statistical models for the multivariate normal covariance matrix will be developed.
Models for the multivariate normal covariance matrix encompass such techniques as: factor analysis,
growth curve models, multilevel models, path-models and errors in variables models. The formulation of
these models under inequality constraints should make possible the evaluation of substantive inequality
constrained theory. Issues such as formal Bayesian prior formulation, parameter estimation using sampling
techniques, model selection and multiple group testing will be addressed. Next to articles, the project will
also result in a statistical package which, in addition to the other procedures developed in the VICI project
Learning more from Empirical Data using Prior Knowledge, will also encapsulate inequality constrained
Bayesian statistics for models based on the multivariate normal covariance matrix.
68
4 Students and projects
Nonlinear modeling with high volume data sets from systems biology
PhD student
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Voice
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Supervisor
Project running from
Project financed by
Ralph Rippe
Data Theory Group, Department of Educational Sciences, Faculty of Social and
Behavioural Sciences, Leiden University, P.O. Box 9555, 2300 RB Leiden
071 527 1782 / 4105 (secretary)
rrippe@fsw.leidenuniv.nl
Prof. dr. J.J. Meulman, prof. dr ing. P.H.C. Eilers
1 June 2006 - 1 June 2011
Leiden University
Summary
Prediction problems are typically regression problems and supervised classification problems, in which the
development of the prediction procedures and their validation go hand-in-hand. Prediction problems are
nonlinear when categorical (ordinal or nominal) variables are involved, possibly with numerical variables as
well.
Large data sets generally come into two forms: either the number of variables is very large compared to the
number of observations (wide data sets), or the number of observations is extremely large (long data sets).
The current proposal will develop, extend and apply methodology to deal with both forms of large data
sets, in a direction which is especially applicable to categorical data through the use of nonlinear
transformations. This approach is firmly based in the data analytic and algorithmic tradition of the Data
Theory Group at the Faculty of Social and Behavioral Sciences at Leiden University.
69
IOPS annual report 2010
The incremental value of Item Response Theory to personality assessment
(new project)
PhD student
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Voice
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Supervisors
Project running from
Project financed by
Iris Smits
Heijmans Institute, Faculty of Behavioural and Social Sciences
University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen
050 363 7996 / 6366 (secretary)
i.a.m.smits@rug.nl
Prof. dr R.R. Meijer, dr. M.E. Timmerman
1 November 2009 - 1 November 2013
University of Groningen
Summary
Psychological assessment is one of psychology’s major contributions to everyday life. An important part of
psychological assessment is personality assessment which is a professional activity of numerous research,
clinical, and industrial psychologists.
In personality assessment often self-report inventories or scales are used. Scale construction and revision
within the field of personality measurement relies heavily on classical test theory (CTT) and factor analytic
methods. Though CTT methods of scale development and scoring have served personality measurement
reasonably well over the last 80 years, CTT has serious limitations and shortcomings (see, for instance,
Fischer, 1974). These limitations and shortcomings are related to the fact that CTT is a model for the test
performance of a randomly drawn respondent from some well-defined population where the influence of
the ability level of the respondent and the influence of the difficulty of tests or items on the test score are
not separated. In item response theory (IRT, for an overview, see van der Linden & Hambleton, 1997), on
the other hand, the influence of respondents and test items are explicitly modeled by different sets of
parameters. This model property proved essential for such activities as linking and equating measurements
and evaluation of test bias and differential item functioning. Further, it provided the underpinnings for item
banking, optimal test construction, and various flexible test administration designs, such as multiple matrix
sampling, flexi-level testing, and computerized adaptive testing. Therefore, in the last decades IRT modeling
has rapidly become the theoretical basis for educational assessment and assessment of cognitive ability.
In psychology, the development of personality and attitude questionnaires through IRT is almost nonexisting although these models are becoming more popular (e.g., Reise & Waller, 2009; Egberink & Meijer, in
press; Meijer, Egberink, Emons, & Sijtsma, 2008). This is unfortunate because the requirements with
respect to the objectivity, reliability and validity of psychological assessment are increasing.
In this project, we explore the incremental value of IRT to the assessment of personality and psychopathology.
70
4 Students and projects
Countering sample selection biases in social and behavioral research
(project completed beyond project’s time limits)
PhD student
Affiliation
Project financed by
Project running from
Date of defence
Title of thesis
Promotores
Marieke Spreeuwenberg
Department of Methodology, Faculty of Social Sciences, Tilburg University
Tilburg University
1 December 2002 - 1 September 2008
29 October 2010
Selection bias in (quasi-) experimental research
Prof. dr. J.A.P. Hagenaars, dr. M.A. Croon
Summary
This project will make an inventory of the different methods that have been proposed to correct for sample
selection bias. The assumptions underlying these correction procedures and their mutual relationships will
be studied, and their applicability in social and behavioral research will be assessed by using simulation
research and by comparing their performance on existing data sets.
71
IOPS annual report 2010
Higher measurement quality of tests and questionnaires by means of more powerful statistics
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Hendrik Straat
Department of Methodology, Faculty of Social Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 8046 / 2544 (secretary)
j.h.straat@uvt.nl
Dr. A. Van der Ark, prof. dr. K. Sijtsma, prof. dr. B.W. Junker
1 September 2009 - 1 September 2012
Tilburg University
Summary
Tests or questionnaires are often used to measure personality traits, attitudes, opinions, skills, and abilities.
A measurement model transforms the respondents’ item scores into a meaningful measurement value.
Using a measurement model that does not fit the data may lead to incorrect conclusions with possibly
severe consequences: e.g., a wrong diagnosis of a mental patient or an incorrect educational placement.
For nonparametric item response theory models - a very general class of measurement models - the
available methods to assess fit are insufficient to allow good test construction. In this project better
methods are developed that have more power.
72
4 Students and projects
Uniqueness, rank, and simplicity of three-way arrays with symmetric slices
(project completed)
PhD student
Affiliation
Project financed by
Project running from
Date of defence
Title of thesis
Promotores
Jorge Tendeiro
Department of Psychology, Faculty of Behavioural and Social Sciences, University of
Groningen
University of Groningen, funded with grant by FCT (Fundaçāo para a Ciência e a
Tecnologia, Portugal)
1 October 2006 - 1 October 2010
28 October 2010
Some mathematical results on three-way component analysis
Prof. dr. J.M.F.Ten Berge, prof. dr. H.A.L. Kiers
Summary
For the analysis of three-way data, various generalizations of Principal Components Analysis have been
proposed. Tucker (1966) proposed 3-mode PCA, which decomposes a data array as a weighted sum of rankone arrays (outer products of columns of matrices A, B and C), the weights for each outer product being an
element of the so-called core array. Carroll and Chang (1970) and Harshman (1970) independently
proposed a method which they christened Candecomp and Parafac, respectively. We refer to it as CP. It can
be verified that CP is a special case of 3-mode PCA.
There is a great need to study more deeply several algebraic properties regarding three-way arrays, such as
uniqueness, typical rank, and simplicity. These properties are crucial for a better understanding of the
application of methods such as CP. In particular, attention will be devoted to three-way arrays with symmetric slices.
73
IOPS annual report 2010
A comparison between factor analysis and item response theory modeling in scale
construction
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Janke Ten Holt
Heijmans Institute, Faculty of Behavioural and Social Sciences, University of
Groningen, Grote Rozenstraat 31, 9712 TG Groningen
050 363 6199 / 6469 (secretary)
j.c.ten.holt@rug.nl
Dr. A. Boomsma, dr. M.A.J. Van Duijn, dr. M.E. Timmerman, prof. dr. T.A.B. Snijders
1 September 2005 - 1 November 2010
NWO (Netherlands Foundation of Scientific Research)
Summary
In practice, the two most important approaches for scale construction are either based on factor analytic or
item response theory modeling. The proposed research deals with a comparative analysis of both methods,
which differ in their assumptions, but share similar principles and coinciding goals. The stability and
sensitivity of scales obtained with each method, are topics of investigation. The analysis of empirical data is
part of the research design. The aim is to develop, so far lacking, practical recommendations for the applied
researcher in a perhaps unified approach.
74
4 Students and projects
Constant latent odds-ratios models for the analysis of discrete psychological data
PhD student
Address
Voice
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Supervisor(s)
Project running from
Project financed by
Jesper Tijmstra
Methodology and Statistics, Faculty of Social and Behavioural Sciences
Utrecht University, P.O. Box 80140, 3508 TC Utrecht
030 253 1490 / 4438 (secretary)
j.tijmstra@uu.nl
Prof. dr. P.G.M. Van der Heijden, prof. K. Sijtsma, dr. D.J. Hessen
1 September 2008 -1 September 2013
Utrecht University
Summary
The main objective of this project is developing statistical procedures for Constant Latent Odds-Ratios
models (CLORs) for dichotomous item scores. Since under dichotomous CLORs models the total score, i.e.,
the unweighted sum of the item scores, is a sufficient statistic for the latent variable, sound statistical
procedures for estimation and goodness of fit assessment are readily attainable. The development of such
procedures will make the CLORs models available for practical use. Furthermore, the characteristic
assumption of constant latent odds-ratios will be used to define new models for polytomous item scores
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IOPS annual report 2010
Multiple imputation using mixture models (new project)
PhD student
Address
Voice
E-mai
Supervisor(s)
Project running from
Project financed by
Daniel Van der Palm
MTO, Tilburg School of Social and Behavioral Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 3270 / 2544 (secretary)
d.w.vdrpalm@uvt.nl
Prof. dr. J.K. Vermunt, prof. dr. K. Sijtsma, dr. L.A. Van der Ark
1 September 2009 - 1 September 2013
NWO (Netherlands Organisation for Scientific Research)
Summary
The main focus of this project is on the use of mixture models for multiple imputation (MI) of missing data,
or more specifically, item nonresponse. Vermunt, Van Ginkel, van der Ark, and Sijtsma (2008) explored the
use of a simple latent class model (Goodman, 1974), which is a mixture model for categorical response
variables, as a tool for MI.
Despite of being a very promising approach, various issues remain unresolved when applying mixture
models for MI. The purpose of this project is to address four unresolved problems mentioned by Vermunt
et al. (2008) in the discussion section of their article:
1. Whereas Vermunt et al. (2008) concentrated on imputation of data sets containing only categorical
variables, most data sets contain combinations of categorical and continuous variables. The current
project will investigate how imputation by means of mixture models can best be generalized to such
mixed data sets.
2. It is not clear at all whether the decision which statistical model explains the data best (also known as
model selection) in the context of mixture modeling for generating multiple imputations can be taken
in the same way as when applying mixture models to build a substantively meaningful model. More
specifically, standard model selection statistics such as information criteria (AIC, BIC) and overall
goodness-of-fit tests seem to be less appropriate for deciding whether a model is a good imputation
model.
3. An extended comparison between MI with mixture models and other MI approaches is lacking. In
order to assess the usefulness of our approach, it is important to investigate in which situations it
performs better than possible alternatives, such as MICE and hot deck imputation.
4. As most of the work on MI, the article by Vermunt et al. (2008) dealt with imputation of data sets
containing independent observations. However, many studies in the social and behavioural sciences
use designs yielding dependent observations, examples of which are studies using multilevel designs
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and longitudinal designs. A fourth aim of this project is to develop mixture MI models for dealing with
such complex designs.
Besides addressing these four topics, the project should yield software implementations so that the MI
methodology becomes available for applied researchers. We aim for making SPSS macro’s available as
freeware on the Internet.
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IOPS annual report 2010
Prior knowledge (project completed)
PhD student
Affiliation
Project financed by
Project running from
Date of defence
Title of thesis
Promotor
Rens Van de Schoot
Methodology & Statistics, Faculty of Social and Behavioural Sciences
Utrecht University
NWO (Netherlands Foundation of Scientific Research)
Part of Vici project by H.J.A. Hoijtink, Learning more from empirical data using prior
knowledge.
1 May 2007 -1 November 2011
29 October 2010
Informative hypotheses: How to move beyond classical null hypothesis testing
Prof. dr. H.J.A. Hoijtink
Summary
Project 1: Prior knowledge
Researchers often have prior theories, knowledge or expectations regarding the possible outcomes of their
analysis of empirical data. In this project prior knowledge and its formalization in inequality constrained
models will be studied from a methodical point of view.
Project 1.1: Prior knowledge: A literature review
In this sub-project a literature study will be executed to elaborate the examples from the beginning of
Section 2a with examples from other leading journals in the social and behavioral sciences. The result will
be an up to date inventory of the manner in which researchers use their theories, knowledge and
expectations when formulating and evaluating null, alternative (that is unconstrained) and informative
hypotheses. Attention will be given to three methodical issues: how theories can be translated into
informative hypotheses using inequality constraints; how researchers use the statistical theory that is
currently available to deal with these informative hypotheses; and, whether or not traditional null and/or
alternative hypotheses are useful in addition to the set of informative hypotheses. Attention will also be
given to the claim of Hoijtink (2005) that the inferences obtained from empirical data using statistical
models will reveal more about reality if data and models are enhanced with prior knowledge in the form of
inequality constraints among the parameters of statistical models. Two aspects will receive attention: the
usefulness and meaning of simplicity (Popper, 1962; Sober, 2002; Howson, 2002) formalized by means of
inequality constrained models for the falsification and selection of theories; and, whether the research led
to a validation of one of the theories or that theory change (Romeyn, 2005, Chapter 8; to appear) is
necessary.
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Project 1.2: Formalizing prior knowledge into inequality constrained models
The paper resulting from Project 1.1 will form the basis for a discussion with scientists from Utrecht
University. First of all, a dataset of each of the scientists will be discussed. The main questions will be: how
they would translate their theories and hypotheses in a set of informative hypotheses; whether or not they
would like to add a traditional null and alternative hypothesis; and how they would currently analyze their
data. Secondly, each scientist will be asked: to describe two or three contemporary and competing theories
that are relevant for their research domain; to name two or more datasets that can be used to evaluate
these theories; and, to formalize these theories into informative hypotheses that can be evaluated using
these datasets. Momentarily, Prof. Meeus (child and adolescent studies), Prof. van der Lippe (sociology),
Prof. van den Hout (clinical psychology) and Prof. van den Bos (social psychology) have agreed that they
and members from their group will participate, but other scientists will also be asked to participate.
The result of this project will be exemplary sets of hypotheses and data sets that will be used in the other
projects and that will be analyzed (Project 1.3 and 1.4) in cooperation with other projects. Furthermore a
paper will be written that will illustrate the various ways in which inequality constraints can be used for the
translation of theories into statistical models.
Project 1.3: Using prior knowledge for the analysis of empirical data: Analysis of variance
The proof of the pudding is in the eating, that is, actual use of a new approach is the best test. In Project 1.3
data sets and informative hypotheses resulting from Project 1.2 will be analyzed in cooperation with
Project 4. The focus will be on hypotheses that can be formalized in analysis of variance type models
enhanced with inequality constraints. The results will be reported to and discussed with the scientists
rendering the data sets and informative hypotheses. The point of departure will again be the question how
they would have analyzed their data before the approach proposed was available. These analyses will
actually be executed. The second question that will be asked is how they value the results obtained with
the new and the old approach. The project will result in a paper explaining and illustrating the main ideas of
the approach proposed to social and behavioral scientists using inequality constrained analysis of variance
type models. Attention will be given to the advantages and disadvantages of the old and proposed
approach from a methodical and statistical point of view.
Project 1.4: Using prior knowledge for the analysis of empirical data: Structural Equation Modeling
Currently, structural equation modeling (as implemented in software like LISREL, AMOS and Mplus) is the
main tool available to social and behavioral scientists who want to formalize theories into a set of
competing models. The main modeling feature is the presence or absence of relations between observed
and latent variables.
In this subproject, data sets and hypotheses resulting from Project 1.2 will be used to introduce, explain
and illustrate (in cooperation with Project 5) structural equation modeling enhanced with inequality
constraints to social and behavioral scientists. The result is a flexible modeling tool in which relations
between variables can not only be absent or present, but also weaker (<) or stronger (>) than other
relations. This subproject will focus on the situation were the best of a number of competing inequality
constrained structural equation models has to be selected.
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IOPS annual report 2010
Methods for making classification decisions (new project)
PhD student
Address
Voice
E-mail
Supervisor
Project running from
Project financed by
Maaike Van Groen
Psychometric Research Center (POC), CITO, P.O. Box 1034, 6801 MG Arnhem
026 352 1464 / 1124 (secretary)
maaike.vanGroen@cito.nl
Prof. dr T.J.H.M. Eggen
1 September 2009 – 1 September 2013
Cito /RCEC (Twente University)
Summary
Most adaptive tests are constructed in order to estimate the examinees’ ability as efficient and accurate as
possible. Computerized classification testing has a different goal: classify the examinee as efficient and
accurate as possible into mutual exclusive groups. Computerized classification testing will be investigated in
this PhD project. Computerized classification tests (CCT) are computerized adaptive tests (CAT) that select
items sequentially for each examinee in order to make a classification decision. The test are also denoted in
the literature as sequential mastery tests (SMT). Traditionally, CATs have the goal of estimating the
respondent’s ability as accurate as possible, but CCTs have the goal of classifying respondents into groups.
A classification decision is made in which the examinee is assigned into one of two or more mutually
exclusive categories along the ability scale (Lin & Spray, 2000) using cutting points to separate the
categories (Eggen, 1999).
A computerized classification test is of variable length and examinees ‘’are classified as masters or nonmasters as soon as there is enough evidence to make a decision’’ (Finkelman, 2008). The classification
procedure must choose between three options: to stop testing and classify an examinee as a master, to
stop testing and classify an examinee as a non-master, or to continue testing and select a new item. Several
procedures are available for making the decisions but also for the way in which items are selected. Six
research topics have been formulated for this project. The six research topics are:
- A multiple objective stochastic curtailed sequential probability ratio test with exposure control
- Multidimensional classification decisions
- Exploring methods for classification decisions
- Making classification decisions on infomation about future items
- Classification decisions using latent class models
- Sequential mastery testing methods for respondents near the cutting point.
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4 Students and projects
Validity of psychological questionnaires (new project)
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Ruud Van Keulen
MTO, Tilburg School of Social and Behavioral Sciences, Tilburg University
P.O. Box 90153, 5000 LE Tilburg
013 466 3270 / 2544 (secretary)
r.j.h.vankeulen@uvt.nl
Prof. dr. K. Sijtsma, prof. dr.S.S. Pedersen
1 April 2009 - 1 April 2013
Tilburg University
Summary
Medical psychologists construct and use multi-item questionnaires for the measurement of attributes. For
example, the Hospital Anxiety and Depression Scale (Zigmond & Snaith, 1983) measures anxiety and
depressive symptoms, the DS-14 (Denollet, 2005) measures type-D personality, the World Health
Organization Quality-of-Life Scale (the WHOQoL Group, 1998) measures quality of life, and the Fatigue
Assessment Scale (Michielsen, De Vries, Van Heck, Van de Vijver, & Sijtsma, 2004) measures fatigue. Such
questionnaires are used, for example, to study the role personality characteristics play in somatic disease,
and the psychological symptoms (e.g., fatigue), which may result from chronic physical disease, psychiatric
disorders, or temporary physical conditions. A crucial issue in questionnaire development and application is
whether the questionnaire adequately measures the attribute of interest. This is the issue of construct
validity.
Construct validity is considered to be of the utmost importance, but psychologists often use outdated
validation procedures. In particular, the approach to construct validity proposed by Cronbach and Meehl
(1955) is still the most popular in use nowadays. Cronbach and Meehl (1955) argued that the existence of
an attribute’s nomological network is a prerequisite for the questionnaire’s validation. The validation
process entails the empirical testing of the relations in the nomological network. This approach to validation has met with considerable criticism, and the recent publication of both critical and influential papers
(e.g., Borsboom, Mellenbergh, & Van Heerden, 2004; Embretson & Gorin, 2001; Kane, 2001; McGrath,
2005; Zumbo, 2007) confirms that the discussion on the theory of validity is at the center of attention in
current psychometrics.
The approach by Cronbach and Meehl (1955) has been criticized for one reason in particular (Messick,
1989). A strict interpretation of nomological networks prohibits researchers to validate tests for which the
attribute is not sufficiently incorporated into a well-developed nomological network. Unfortunately, this
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IOPS annual report 2010
applies to many attributes. For example, in the context of medical psychology the attribute of fatigue lacks
a sound theoretical basis (Michielsen et.al., 2004; also, see Barofsky & Legro, 1991).
Because of its shortcomings, Cronbach and Meehl’s (1955) validity approach is difficult to implement in
psychological research. Alternatively, researchers take refuge to development of questionnaires on the
basis of tradition, habit or intuition, and use few theory-driven guidelines (Sijtsma, in press). Fatigue is an
excellent example of an attribute, which is poorly supported by sound theory but for which many questionnaires are available and in frequent use, which all claim to measure fatigue or a fatigue related attribute.
Examples of questionnaires are the emotional exhaustion subscale from the Maslach Burnout Inventory
(MBI; Maslach & Jackson, 1986), the Multidimensional Assessment of Fatigue scale (MAF; Piper, Lindsey,
Dodd, Ferketich, & Weller, 1989), and the Checklist Individual Strength (CIS-20; Vercoulen, Alberts, &
Bleijenberg, 1999).
The lack of sound attribute theory underlying the development of questionnaires affects validation
practices in the contemporary research setting. Often construct validity is ascertained by means of highly
explorative research strategies. For example, exploratory factor analysis is much used to investigate the
structure of the data, and the finding that correlations exist between certain items is presented as evidence
that an attribute is measured. Consequently, construct validation is not based on solid theory, but is mainly
data-driven.
The process of construct validation proposed by Cronbach and Meehl (1955) is valuable in theory but
unsuited for contemporary validity research. Schouwstra (2000, chap. 1) noticed that also novel approaches to validity theory are often difficult to implement in practice. Attempts have been made in cognitive
psychology but we know of no attempts in medical psychological research. Hence, the aim of this
dissertation project is the development of guidelines for questionnaire validation in medical psychology
using modern approaches that overcome the shortcomings of the nomological network approach
(Cronbach & Meehl, 1955).
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4 Students and projects
Modeling the relation between speed and accuracy
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Don Van Ravenzwaaij
Psychological Methodology, Department of Psychology
FMG, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam
020 525 8870 / 6870 (secretary)
d.vanravenzwaaij@uva.nl
Dr. E.J. Wagenmakers, prof. dr. H.L.J. van der Maas
1 January 2008 - 1 January 2012
NWO (Netherlands Foundation of Scientific Research)
Summary
In daily life as well as in the psychological laboratory, people continuously make decisions. These decisions
pertain to widely different activities, such as buying new sun-glasses, driving your car to work, or writing
grant proposals. All of these decisions, however, fall prey to the same dilemma. This dilemma concerns the
meta-decision of when to stop information processing and commit to a decision. This is particularly evident
in tasks where one can choose to respond faster at the cost of making more errors. Clearly then, task
performance is a function of both response accuracy and response speed. A pervasive problem in cognitive
psychology is how to combine speed and accuracy so as to obtain separate indices for task performance
and response conservativeness.
Perhaps the only way to make progress is to use a mathematical model that explicitly addresses the
tradeoff between speed and accuracy. The current proposal focuses on Ratcliff's diffusion model, which is
arguably the most popular model of how people process information. The diffusion model allows one to
estimate unobserved psychological processes such as perception, speed of information accumulation,
response conservativeness, and response bias.
The proposed projects seek to theoretically extend and empirically test the diffusion model account of the
speed-accuracy tradeoff. This account currently leaves open several important questions. The first project
shows that the Fuzzy Logical Model of Perception (FLMP) can be unified with the diffusion model in a way
that allows the FLMP to simultaneously account for response speed and response accuracy. The second
project studies what happens under conditions in which there is almost no value in accurate responding.
The third project considers variability in response conservativeness as an explanation for fast errors, and
the fourth project concerns the changes in information processing that occur after an error.
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IOPS annual report 2010
Model selection
PhD student
Address
Voice
E-mail
Supervisor
Project running from
Project financed by
Floryt Van Wesel
Methodology & Statistics, Faculty of Social and Behavioural Sciences
Utrecht University, P.O. Box 80140, 3508 TC Utrecht
030 253 1571 / 4438 (secretary)
f.vanwesel@uu.nl
Prof. dr. H.J.A. Hoijtink, dr. I.G. Klugkist
September 2006 - 1 September 2011
NWO (Netherlands Foundation of Scientific Research)
Part of Vici project by H.J.A. Hoijtink “Learning more from empirical data using prior
knowledge”
Summary
This project is part of a bigger research project about the use of prior knowledge, Bayesian statistics.
Researchers using prior knowledge either end up with a set of competing models that differ in the
inequality constraints used, or with one or more constrained models, a null model and an unconstrained
model. In this project several model selection criteria that can be used to select the best model will be
developed, studied and evaluated.
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4 Students and projects
Bayesian modeling of heterogeneity for large scale comparative research
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Josine Verhagen
Department of Educational Measurement and Data Analysis, Faculty of Educational
Science and Technology, Twente University, P.O. Box 217, 7500 AE Enschede
053 489 3581 / 3555 (secretary)
a.j.verhagen@utwente.nl
Prof. dr. C.A.W. Glas, dr. ir. G.J.A. Fox
1 May 2008 - 1 May 2012
Twente University
Summary
Inferences from large-scale (e.g., cross-national) studies have important implications for theory
(e.g., causal relations between constructs, spurious relations, intervening variables) and practice
(e.g., insights in policy related issues and malleable factors). The common item response theory
models are not directly applicable to analyse large-scale survey data for comparative research.
There are several measurement issues connected to comparative research that need to be addressed since ignoring them may lead to inferential errors. The approach is focused on delineating
the source (i.e., individual or group differences in latent scores or in the way of responding to the
questionnaire) and the direction of the significant differences in cross-national research. From a
Bayesian point of view, (1) heterogeneity in the way individuals respond to the questionnaire is
modelled. In addition, (2) a structural population model is built for the respondents’ latent scores
which is focused on heterogeneity. Within this modelling framework, the Bayesian methodology
allows the development of tools that can be used to account for errors related to the measurement issues.
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IOPS annual report 2010
Restrictive imputation of incomplete survey data (new project)
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Gerko Vink
Methodology and Statistics, Faculty of Social Sciences, Utrecht University
P.O. Box 80140, 3508 TC Utrecht
30 253 9140 / 4438 (secretary)
g.vink@uu.nl
Prof. dr S. Van Buuren, dr. J. Pannekoek, dr. L.E. Frank
1 September 2009 - 1 September 2013
Utrecht University and Statistics Netherlands (CBS)
Summary
Imputation is a method to correct for missing data by using various models to estimate missing values
whilst adding the estimated data to the original dataset. The completed dataset can then be analyzed by
methods for complete data. To estimate the reliability of estimates on imputed data, however, special
techniques are needed, because standard methods for complete data do not discriminate between real
and imputed data.
Imputations are predictions for the values that could have been encountered, if the missing data would
have been observed. Because imputations are, to some extent, used as real observations, these predictions
have to be as accurate as possible. In order to obtain accurate estimates, models have to be constructed
that optimally represent the properties of the various variables and their internal coherence. In addition to
the quality of predictions, plausible imputations also have to meet certain a priori knowledge, such as
variable restrictions (e.g. an income must be greater than or equal to zero) or restrictions conform to
known population distributions (e.g. the known amount of cars in a country).
Three research topics will be distinguished in this research proposal: imputing variables that have to meet
restrictions (§A), imputing semi-continuous variables (§B) and measuring the quality of imputation models
and the accuracy and reliability of estimations on imputed data (§C). These research questions can be
answered within a PhD position, resulting in a dissertation, as well as new software. Expected results
include answering the following general research questions:
- How can imputations under row and column restrictions be executed?
- How can imputations on semi-continuous data best be done?
- How can imputations most effectively and plausibly be evaluated?
Furthermore, based on the research in this PhD-project, recommendations for routinely use of imputation
methods at Statistics Netherlands will be made.
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4 Students and projects
EEG/MEG components: A new statistical approach to analyze their (co)variance
properties
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Wouter Weeda
Developmental Psychology, University of Amsterdam
Roetersstraat 15, 1018 WB Amserdam
020 525 6908 / 6830 (secretary)
w.d.weeda1@uva.nl
Prof. dr. M.W. Van der Molen, dr. H.M. Huizenga
1 March 2006 - 1 December 2010
NWO (Netherlands Foundation of Scientific Research)
Part of Vidi project by Hilde Huizenga “The association between intelligence and
performance variability: A new statistical neuroscientific approach”
Summary
In this project the primary aim is to assess variance and covariance properties of EEG/MEG components,
without the need to localize these components. Such a method should meet several criteria. First, it is
necessary that signal variance can be dissociated from noise variance. Second, it should be possible to
disentangle latency variance and tests of amplitude and latency variance parameters. Third, it is neccessary
that the amplitude covariance between components can be estimated and tested. Existing methods (e.g.
variance, complexity, wavelets, independent component analysis, parallel factor analysis) are adequate to
answer other reseacrh quetions, but they do not meet the aforementioned criteria, and thus are not suited
for the present purposes.
We therefore develop a new statistical method that does meet these criteria. By modeling EEG/ MEG by a
sum of a) partly random temporal component functions and b) a noise variance model, it will become
possible to reliably assess variations in amplitude and latency, and the covariance of amplitudes. Since the
proposed method is new and by no means straigthforward, it will be developed in several subprojects that
have substantial merits in their own right.
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IOPS annual report 2010
Modeling the relation between speed and accuracy
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Ruud Wetzels
Psychological Methodology, Department of Psychology, FMG
University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam
020 525 8871 / 6870 (secretary)
r.m.wetzels@uva.nl
Dr. E.J. Wagenmakers, prof. dr. H.L.J. van der Maas
1 September 2008 - 1 September 2012
University of Amsterdam
Summary
One goal of this PhD project is to do Bayesian inference using all kinds of models that are popular in
Psychology. Some examples of such models are ALCOVE (Kruschke, 1992) for category learning or the
Expectancy-Valence model (Busemeyer and Stout, 2002) for decision making.
Another goal of the project is to implement and study Bayesian hypothesis testing for hierarchical, possibly
order-restricted models. In hierarchical modeling, individual-level parameters are drawn from a group
distribution. This way of modeling takes both differences and similarities between participants into
account.
In general, the aim is trying to make Bayesian methods more easily available to empirically oriented
psychologists who would like to take advantage of the Bayesian methodology but lack the time or the
technical skills to implement their own software.
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4 Students and projects
Admissable statistics and latent variable theory
PhD student
Address
Voice
E-mail
Supervisors
Project running from
Project financed by
Annemarie Zand Scholten
Psychological Methodology, Department of Psychology, FMG
University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam
020 525 6880 / 6870 (secretary)
a.zandscholten@uva.nl
Prof. dr. H.L.J. van der Maas, dr. D. Borsboom, dr. P. Koele
1 April 2005 - 1 September 2010
NWO (Netherlands Foundation of Scientific Research)
Summary
Does the appropriateness of statistical analyses depend on the measurement level of the variables on
which these analyses are carried out? Measurement theorists are generally of the opinion that this is the
case; the measurement level of variables determines the class of appropriate statistics. Several statisticians
have, however, claimed that this limitation is unfounded and that it has adverse scientific consequences.
The disagreement between these camps is known as the admissible statistics controversy. Although discussants in this controversy disagree on virtually everything, they share a core assumption: namely, that
measurement and statistical analyses are separate endeavors. However, in an important class of measurement models, known as latent variable models, measurement and statistical theory are intertwined to such
a degree that it is difficult to say where one begins and the other ends. In the present research, the admissible statistics problem is formulated and analyzed in terms of latent variable theory. This yields a quite
different view of what the problem actually is; namely, a problem that occurs because statistical analyses
assume that variables are errorless measures of the theoretical attributes involved, while measurement
models usually view these same variables as imperfect indicators of these attributes. Thus, the admissible
statistics problem becomes a question of robustness: Under which conditions is it possible to ignore
measurement error and equate observed scores to theoretical attributes? This question is investigated
through mathematical analysis and simulation studies. Second, alternative methodes of analysis, that may
be used to address measurement and statistical issues at the same time, are evaluated for their potential in
solving the admissible statistics problem; specifically, the use of multi group models with mean structures
in factorial designs is investigated in this respect.
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5
Graduate training program
5.1
Courses in the IOPS curriculum
In 2010 two IOPS courses were organized:
- Optimization & numerical methods in statistics: Concepts, models, and applications
Instructors: Francis Tuerlinckx and Geert Molenberghs (K.U.Leuven)
Dates: 22-24 November 2010
- Analysis of measurement instruments: Introduction to classical test theory, item response models and
multilevel item response models
Instructors: Cees Glas, Jean-Paul Fox, Marianna Avetisyan
Dates: 15-18 November 2010
5.2
Conferences
5.2.1 25th IOPS summer conference
The 25th IOPS summer conference was held in Amsterdam on 10-11 June 2010. University of Amsterdam
(UvA), co-organiser and host of the conference, welcomed 49 participants.
Invited speakers
An invited presentation was given by:
- Han Van der Maas University of Amsterdam.
Title: θ ≥ 0.
Other conference presentations
The following 9 IOPS PhD students gave a presentation on the results of their research:
- Marjan Bakker, University of Amsterdam
Title: The (mis)reporting of statistical results in psychology journals
- Angelique Cramer, University of Amsterdam
Title: Mental disorders as networks
- Kasia Jozwiak, Utrecht University
Title: Cost-efficient designs for trials with discrete-time survival endpoints
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IOPS annual report 2010
- Bellinda King Kallimanis, University of Amsterdam / AMC
Title: Using structural equation modelling to detect measurement bias and response shift in longitudinal
data
- Peter Kruyen, University of Tilburg
Title: Test length and decision quality in personnel selection
- Baerbel Maus, Maastricht University
Title: Optimal and robust design of fMRI experiments with application of a genetic algorithm
- Jorge Tendeiro, University of Groningen
Title: First and second order derivatives for CP and INDSCAL
- Jesper Tijmstra, Utrecht University
Title: Testing manifest monotonicity using constrained statistical inference
- Josine Verhagen, Twente University
Title: Bayesian tests for measurement invariance in large cross-national surveys
Other activities
IOPS Best paper award 2009
During the 25th IOPS summer conference, the IOPS Best Paper Award 2009 was delivered to Jorge Tendeiro, University of Groningen, for his paper: Tendeiro, Jorge N., Ten Berge, Jos M.F., & Kiers, Henk A.L.
(2009). Simplicity transformations for three-way arrays with symmetric slices, and applications to Tucker-3
models with sparse core arrays. Linear Algebra and its Applications, 430, 924–940.
5.2.2 20th IOPS winter conference
The 20th IOPS winter conference was held on 16 and 17 December 2010 at Utrecht University. Utrecht
University, co-organiser and host of the conference, welcomed 66 participants.
Conference presentations
Invited speakers
- Hennie Boeije, Utrecht University (Keynote address)
Title: Comparing the incomparable? Integrating qualitative and quantitative research
- Jorge Tendeiro, winner of the IOPS Best paper Award 2009, University of Groningen.
Title: Simplicity transformations for three-way arrays with symmetric slices, and applications to Tucker-3
models with sparse core arrays
Other speakers
The following ten IOPS PhD students gave a presentation on the topic of their research:
- Marianna Avetisyan, Twente University
Title: Application and validation of the randomized response technique
- Britt Qiwei He, Twente University
Title: Automated classification of patients’ self narratives for PTSD diagnosis
- Marian Hickendorff, Leiden University
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5 Graduate training program
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-
-
Title: The language factor in mathematics assessments: The influence of contexts in arithmetic problems
Suzanne Jak, University of Amsterdam
Title: A test for cluster bias: Detecting violations of measurement invariance across clusters in multilevel
data
Elly Korendijk, Utrecht University
Title: Sample size requirements when applying a Fishbein-Ajzen Model in cluster randomized trials:
A simulation study
Rudy Ligtvoet, University of Amsterdam
Title: Latent scales for investigating the ordering of items
Meike Morren, Tilburg University
Title: “Give me an example and I’ll tell you what I think”
Joris Mulder, Tilburg University
Title Bayesian model selection of Informative hypotheses for repeated measurements
Ralph Rippe, Leiden University
Title: SCALA: A statistical framework for SNPs
Hendrik Straat , Tilburg University
Conditional association as a powerful tool for assessing IRT model fit
Other activities
Lab meeting (short presentations by Utrecht PhD students)
Chairs: Herbert Hoijtink (Applied Bayesian Statistics), Joop Hox (Social Science Methodology), and Dave
Hessen (Statistics for Social and Behavioral Sciences)
Survey methodology
- Thomas Klausch: Measurement and nonresponse error in mixed-mode surveys: disentangling, modeling
and correcting
- Vanessa Torres van Grinsven: Motivation of Respondents in Business Surveys
Bayesian statistics
- Rebecca Kuiper: Model Selection Criteria: How to Evaluate Order Restrictions
- Carel Peeters: Objective Prior Predictive Densities for Constrained Model Selection: WithApplications to
Gaussian-Theory Factor Analytic Modeling
- Charlotte Rietbergen: Improving epidemiological research by Bayesian analysis
- Floryt Van Wesel: Priors & Prejudice: Using existing knowledge in social science research
Statistics for social and behavioral sciences
- Shahab Jolani: Missing Data and Multiple Imputation
- Kasia Jozwiak: Optimal designs for trials with discrete-time survival endpoints
- Elly Korendijk: Robustness properties for cluster randomized trials
- Jesper Tijmstra: Generalizing the Rasch model
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IOPS annual report 2010
94
6
Publications
A quantitative overview and a list of publications by IOPS staff members and PhD students under auspices
of IOPS in 2010 is given below.
Quantitative overview of publications in 2010
Dissertations by IOPS PhD students
Other dissertations under supervision of IOPS staff members
Articles in international English-language journals
Contributions to international English-language volumes
Book reviews
Books and test manuals
Articles in other journals
Software and test manuals
Other publications
6.1
6
3
219
52
1
1
16
3
26
Dissertations
6.1.1 Dissertations by IOPS PhD students
Ligtvoet, R. (2010). Essays on invariant item ordering. Tilburg University (115 pp.). Enschede: Gildeprint.
Prom./coprom.: K. Sijtsma, J.K. Vermunt, & L.A. Van der Ark.
Lukociene, O. (2010). Latent class models for categorical data with a multilevel structure. Tilburg University
(108 pag.). Ridderkerk: Ridderprint. Prom./coprom.: J.K. Vermunt.
Mulder, J. (2010). Bayesian model selection for constrained multivariate normal linear models. Utrecht
Universiteit (205 pp.). Utrecht: ZuidamUithof Drukkerijen. Prom./coprom.: H.J.A. Hoijtink, G.J.A. Fox
& I.G. Klugkist.
Spreeuwenberg, M.D. (2010). Selection bias in (quasi-) experimental research. Tilburg University (175 pag.).
Ridderkerk: Ridderprint. Prom./coprom.: J.A.P. Hagenaars & M.A. Croon.
Tendeiro, J.N. (2010). Some mathematical results on three-way component analysis. University of Groningen (133 pp.) Ridderkerk: Ridderprint. Prom./coprom.: J.M.F. Ten Berge & H.A.L. Kiers.
Van de Schoot, R. (2010). Informative hypotheses: How to move beyond classical null hypothesis testing.
Utrecht: Utecht University (256 pp.). Prom./coprom.: H.J.A. Hoijtink
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IOPS annual report 2010
6.1.2 Other dissertations under supervison of IOPS staff members
Busing, F.M.T.A. (2010). Advances in multidimensional unfolding. Leiden University (268 pag.). Enschede:
Gildeprint Drukkerijen. Prom./coprom.: W.J. Heiser.
Egberink, I.J.L. (2010). Applications of Item Response Theory to non-cognitive data. University of Groningen
(137 pag.). Enschede: Ipskamp Drukkers B.V. Prom./coprom.: R.R. Meijer & B.P. Veldkamp.
Kankaras, M. (2010). Essays on measurement equivalence in cross-cultural survey research: A latent class
approach. Tilburg University (196 pag.). Prom./coprom.: J.K. Vermunt & G.B.D. Moors.
6.2
Articles in international English-language journals
Albers, C.J. & Gower, J.C. (2010). A general approach to handling missing values in Procrustes Analysis.
Advances in Data Analysis and Classification, 4, 223-237.
Arief, V.N., Kroonenberg, P.M., DeLacy, I.H., Dieters, M.J., Crossa, J., Dreisigacker, S., Braun H.-J. & Basford,
K.E. (2010). Construction and three-way ordination of the Wheat Phenome Atlas. Journal of Applied
Probability and Statistics, 5, 95-118.
Barendse, M.T., Oort, F.J., Garst, G.J.H. (2010). Using restricted factor analysis with latent moderated
structures to detect uniform and nonuniform measurement bias; a simulation study. Advances in
Statistical Analysis, 94, 117-127.
Bechger, T.M., Maris, G.K.J. &Hsiao, Y.P. (2010). Detecting halo effects in performance-based
examinations. Applied Psychological Measurement, 34, 8, 607-619.
Bekker, M.H.J. & Croon, M.A. (2010). The roles of autonomy-connectedness and attachment styles in
depression and anxiety. Journal of Social and Personal Relationships, 27, 7, 908-923.
Bennani-Dosse, M. & Ten Berge, J.M.F. (2010). Anisotropic orthogonal Procrustes analysis. Journal of
Classification, 27, 111-128.
Bethlehem, J.G. (2010), Selection bias in web surveys. International Statistical Review, 78, 161-188.
Bloemberg, T.G., Gerretzen, J., Wouters, H.J.P., Gloerich, J., Van Dael, M., Wessels, H.J.C.T., Van den Heuvel,
L.P., Eilers, P.H.C., Buydens, L.M.C. & Wehrens, R. (2010). Improved parametric time warping for
proteomics. Chemometrics and Intelligent Laboratory Systems, 104, 1, 65-74.
Bogacz, R., Wagenmakers, E.-J., Forstmann, B.U. & Nieuwenhuis, S. (2010). The neural basis of the speedaccuracy tradeoff. Trends in Neurosciences, 33, 10-16.
Boks, M.P.M., Derks, E.M. Dolan C.V, Kahn, R.S., Ophoff, R.A. (2010). “Forward Genetics” as a method to
maximize power and cost efficiency in studies of human complex traits. Behavior Genetics, 40, 564–
571.
Boomsma, D.I., Van Beijsterveldt, C.E.M., Van Soelen, I.L.C., Franic, S.F., Dolan, C.V., Borsboom, D. &
Bartels, M. (2010). Genetic analysis of longitudinally measured IQ, educational attainment and
educational level in Dutch twin-sib samples. Twin Research and Human Genetics, 13, 3, 248-249.
Boros, S., Meslec, M.N., Curseu, P.L. & Emons, W.H.M. (2010). Struggles for cooperation: Conflict
resolution strategies in multicultural groups. Journal of Managerial Psychology, 25, 2, 539-554.
Borst, G., Kievit, R.A., Thompson, W. L. & Kosslyn, S.M. (2010) Mental rotation is not easily cognitively
penetrable. Journal of Cognitive Psychology, 32, 1, 60-75
Bouwmeester, S. & Verkoeijen, P.P.J.L. (2010). Latent variable modeling of cognitive processes in true and
96
6 Publications
false recognition of words: A developmental perspective. Journal of Experimental PsychologyGeneral, 139, 2, 365-381.
Brosschot, J.F., Pieper, S., Van der Leeden, R., Thayer, J.F. (2010) Prolonged cardiac effects of momentary
assessed stressful events and worry episodes. Psychosomatic Medicine, 72, 6, 570-577.
Busing, F.M.T.A., Heiser, W.J. & Cleaver, G. (2010). Restricted unfolding: Preference analysis with optimal
transformations of preferences and attributes. Food Quality and Preference, 21, 82-92.
Candel, M.J.M.M. & Van Breukelen, G.J.P. (2010). D-optimality of unequal versus equal cluster sizes for
mixed linear regression analysis of randomized trails with clusters in one treatment arm. Computational Statistics and Data Analysis, 54, 1906-1920.
Candel, M.J.M.M. & Van Breukelen, G.J.P. (2010). Sample size adjustments for varying cluster sizes in
cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic
regression. Statistics in Medicine, 29, 1488-1501.
Ceulemans, E. & Storms, G. (2010). Detecting intra and inter categorical structure in semantic concepts
using HICLAS. Acta Psychologica, 133, 296-304.
Chow, S-M., Ho, M.H.R., Hamaker, E.L. & Dolan, C.V. (2010). Equivalences and differences between structural equation modeling and state-space modeling techniques. Structural Equation Modeling, 17, 303332.
Cramer, A.O.J., Waldorp, L.J., Van der Maas, H. & Borsboom, D. (2010). Authors' response. Complex
realities require complex theories: Refining and extending the network approach to mental disorders.
Behavioral and Brain Sciences, 33, 178-193.
Cramer, A.O.J., Waldorp, L.J., Van der Maas, H.L.J. & Borsboom, D. (2010). Comorbidity: A network
perspective. Behavioral and Brain Sciences, 33, 137-150.
De Bruin, D., Viechtbauer, W., Schaalma, H.P., Kok, G., Abraham, C. & Hospers, H.J. (2010). Standard care
impacts of intervention effects in HAART adherence RCTs: A meta analysis. Archives of Internal
Medicine, 170, 3, 240-250.
De Bruin, M., Hospers, H.J., Van Breukelen, G.J.P., Kok, G.J., Koevoets, W.M. & Prins, J.M. (2010). Electronic
monitoring-based counseling to enhance adherence among HIV-infected patients: a randomized
controlled trial. Health Psychology, 29, 421-428.
De Bruin, M., Viechtbauer, W., Schaalma, H.P., Kok, G., Abraham, C. & Hospers, H.J. (2010). Standard care
impacts intervention effects in HAART adherence RCTs: A meta-analysis. Archives of Internal
Medicine, 170, 3, 240-250.
De Jong, K., Moerbeek, M. & Van der Leeden, M. (2010). A priori power analysis in longitudinal three-level
multilevel models: An example with therapist effect. Psychotherapy Research, 20, 3, 273-284.
De Jong, M.G., Pieters, F.G.M. & Fox, G.J.A. (2010). Reducing social desirability bias through item randomized response: An application to measure underreported desires. Journal of Marketing Research, 47,
14-27.
Dewald, J.F., Meijer, A.M., Oort, F.J., Kerkhof, G.A. & Bögels, S.M. (2010). The influence of sleep quality,
sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic
review. Sleep Medicine Reviews, 14, 179-189.
DuMoulin, M.F.M.T., Chenault, M., Tan, F.E.S., Hamers, J.P.H. & Halfens, R.H.G. (2010). Quality systems to
improve care in older patients with urinary incontinence receiving home care: Do they work? Quality
& Safety in Health Care, 19, 1-7.
Edward, G.M., Preckel, B., Martijn, B.S., Oort, F.J., De Haes, H.C.J.M. & Hollmann, M.W. (2010). The effects
of implementing a new schedule at the preoperative assessment clinic. European Journal of
Anaesthesia, 27, 209-213.
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IOPS annual report 2010
Egberink, I.J.L, Meijer, R.R. & Veldkamp, B.P. (2010). Conscientiousness in the workplace: Applying mixture
IRT to investigate scalability and predictive validity. Journal of Research in Personality, 44, 232-244.
Egberink, I.J.L., Meijer, R.R., Veldkamp, B.P., Schakel L. & Smid, N.G. (2010). Detection of aberrant item
score patterns in computerized adaptive testing: An empirical example using the CUSUM. Personality
and Individual Differences, 48, 8,921-925.
Eggen, T.J.H.M. (2010). Sequential Probability Ratio Test. International Encyclopedia of Education. 7, 396400.
Evers, A., Sijtsma, K., Lucassen, W. & Meijer, R.R. (2010). The Dutch review process for evaluating the
quality of psychological tests: History, procedure and results. International Journal of Testing, 10, 4,
295-317.
Forstmann, B.U., Anwander, A., Schäfer, A., Neumann, J., Brown, S., Wagenmakers, E.-J., Bogacz, R. &
Turner, R. (2010). Cortico-striatal connections predict control over speed and accuracy in perceptual
decision making. Proceedings of the National Academy of Sciences, 107, 15916-15920.
Forstmann, B.U., Brown, S., Dutilh, G., Neumann, J. & Wagenmakers, E.-J. (2010). The neural substrate of
prior information in perceptual decision making: A model-based analysis. Frontiers in Human
Neuroscience, 40 , Article 4. (electronic journal)
Franic, S., Middeldorp, C.M., Dolan, C.V., Ligthart, L. & Boomsma, D.I. (2010). Childhood and adolescent
anxiety and depression: Beyond heritability. Journal of the American Academy of Child and
Adolescent Psychiatry, 49, 8, 820-832.
Frederickx, S., Tuerlinckx, F., De Boeck, P. & Magis, D. (2010). RIM: A random item mixture model to detect
Differential Item Functioning. Journal of Educational Measurement, 47, 432-457.
Geelhoed, J.J.M., Taal, R.H.A., Steegers, E.A.P., Arends, L.R., Lequin, M., Hofman, A., Van der Heijden, A.J. &
Jaddoe, V.W.V. (2010). Kidney growth curves in healthy children from third trimester of pregnancy
until the age of two years. The Generation R Study. Pediatric Nephrology, 25, 2, 289-298.
Gelman, A., Leenen, I., Van Mechelen, I., De Boeck, P. & Poblome, J. (2010). Bridges between deterministic
and probabilistic models for binary data. Statistical Methodology, 7, 187-209.
Glas, C.A.W. & Van der Linden, W.J. (2010). Marginal likelihood inference for a model for item responses
and response times. British Journal of Mathematical and Statistical Psychology, 63, 603-626.
Gobbens, R., Van Assen, M.A.L.M., Luijkx, K.G., Wijnen-Sponselee, M.Th. & Schols, J.M.G.A. (2010). The
Tilburg Frailty Indicator: Psychometric properties. Journal of the American Medical Directors
Association, 11, 5, 344-355.
Gobbens, R., Van Assen, M.A.L.M., Luijkx, K.G., Wijnen-Sponselee, M.Th. & Schols, J.M.G.A. (2010).
Determinants of frailty. Journal of the American Medical Directors Association, 11, 5, 356-364.
Goegebeur, Y., De Boeck, P. & Molenberghs, G. (2010). Person fit for test speededness: normal curvatures,
likelihood ratio tests and empirical Bayes estimates. Methodology: European Journal of Research
Methods for the Behavioral and Social Sciences, 6, 3-16
Gonzalez Marin, G.V., Bouwmeester, S. & Boonstra, A.M. (2010). Understanding planning ability measured
by the Tower of London: An evaluation of its internal structure by latent variable modeling. Psychological Assessment, 22, 4, 923-934.
Gower, J.C., Groenen, P.J.F. & Van de Velden, M. (2010). Area biplots. Journal of Computational and
Graphical Statistics, 19, 1, 46-61.
Grasman, R.P.P.P., Huizenga, H.M. & Geurts, H.M. (2010). Departure from normality in multivariate normative comparison: The Cramér alternative for Hotelling's T2. Neuropsychologia, 48, 5, 1510-1516.
98
6 Publications
Grootenboer, N., Sambeek, M.R. van, Arends, L.R., Hendriks, J.M., Hunink, M.G. & Bosch, J.L. (2010). Systmc review and meta-analysis of sex differences in outcome after intervention for abdominal aortic
aneurysm. British Journal of Surgery, 97, 8, 1169-1179.
Hagenaars, M., Van Minnen, A. & De Rooij, M (2010). Cognitions as a mechanism for change in prolonged
exposure treatment for PTSD. International Journal of Clinical and Health Psychology, 10, 421-434.
Haringsma, R., Spinhoven, Ph., Engels, G.I. & Van der Leeden, M. (2010). Effects of sad mood on
autobiographical memory in older adults with and without lifetime depression. British Journal of
Clinical Psychology, 49, 3,343-357.
Hartendorp, M.O., Van der Stigchel, S., Burnett, H.G., Jellema, T., Eilers, P.H.C. & Postma, A. (2010), Categorical perception of morphed objects using a free-naming experiment, Visual Cognition, 18,9, 13201347.
Heathcote, A., Brown, S., Wagenmakers, E.-J. & Eidels, A. (2010). Distribution-free tests of stochastic
dominance for small samples. Journal of Mathematical Psychology, 54, 454-463.
Heinrich, E., Candel, M.J.J.M., Schaper, N.C. & De Vries, N.K. (2010). Effect evaluation of a Motivational
Interviewing based counselling strategy in diabetes care. Diabetes Research and Clinical Practice, 90,
270-278.
Hensel, R., Meijers, F., Van der Leeden, M. & Kessels, J. (2010). 360 degree feedback: How many raters are
needed for reliable ratings on the capacity to develop competences, with personal qualities as
developmental goals? International Journal of Human Resource Management, 21 (13-15), 2813-2830.
Hickendorff, M., Van Putten, C.M., Verhelst, N.D. & Heiser, W.J. (2010). Individual differences in strategy
use on division problems: Mental versus written computation. Journal of Educational Psychology,
102, 2, 438-452.
Houtveen, J.H., Hamaker, E.L. & Van Doornen, L.J. P. (2010). Using multilevel path analysis in analyzing 24hour ambulatory physiological recordings applied to medically unexplained symptoms. Psychophysiology, 47, 570-578.
Hox, J.J., Maas, C.J.M., & Brinkhuis M.J.S. (2010). The effect of estimation method and sample size in
multilevel structural equation modeling. Statistica Neerlandica, 64, 157-170.
Iverson, G., Lee, M.D. & Wagenmakers, E.-J. (2010). The random-effects prep continues to mispredict the
probability of replication. Psychonomic Bulletin & Review, 17, 270-272.
Iverson, G.J., Wagenmakers, E.-J. & Lee, M. D. (2010). A model averaging approach to replication: The case
of prep. Psychological Methods, 15, 172-181.
Jak, S. & Evers, A. (2010). Onderzoeksnotitie: Een vernieuwd meetinstrument voor organizational
commitment. Gedrag en Organisatie, 23, 2, 158-171.
Jak, S., Oort, F.J. & Dolan, C.V. (2010). Measurement bias and multidimensionality; An illustration of bias
detection in multidimensonal measurement models. Advances in Statistical Analysis, 94, 129-137.
Jepma, M., Te Beek, E.T., Wagenmakers, E.-J., Van Gerven, J.M A. & Nieuwenhuis, S. (2010). The role of the
noradrenergic system in the exploration-exploitation trade-off: A pharmacological study. Frontiers in
Human Neuroscience, 4, Article 170. (electronic journal)
Jharap, B., Seinen, M.L., De Boer, N.K.H., Van Ginkel, J.R., Linskens, R.K., Kneppelhout, J.C., Mulder, C.J.J. &
Van Bodengraven, A.A. (2010). Thiopurine Therapy in inflammatory bowel disease patients: Analyses
of two 8-year intercept cohorts. Inflammatory Bowel Diseases, 16, 1541-1549.
Kalmijn, W.M. & Arends, L.R. (2010). Erratum to: Measures of inequality: Application to happiness in
nations. Social Indicators Research, 100, 367-367.
Kalmijn, W.M. & Arends, L.R. (2010). Measures of inequality: Applications to happiness in nations. Social
Indicators Research, 99, 1, 147-162.
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IOPS annual report 2010
Kammers, M.P.M., Mulder, J., De Vignemont, F. & Dijkerman, H.C. (2010). The weight of representing the
body: Addressing the potentially indefinite number of body representations in healthy individuals.
Experimental Brain Research, 204,333-342.
Kan, K.J., Ploeger, A., Raijmakers, M.E.J., Dolan, C.V. & Van der Maas, H.L.J. (2010). Nonlinear epigenetic
variance: Review and simulations. Developmental Science, 13, 1, 11-27.
Kankarash, M. & Moors, G.B.D. (2010). Researching measurement equivalence in cross-cultural studies.
Psyhologija, 43, 2, 121-136.
Kelderman, H., Felius, J. & Passchier, J. (2010). Rasch analysis for QoL questionnaires. Investigative
Ophthalmology & Visual Science, 51, 6898-6899.
Kiefte-de Jong, J.C, Escher, J.C., Arends, L.R., Jaddoe, V.W., Hofman, A., Raat, H. & Moll, H.A. (2010). Infant
nutritional factors and functional constipation in childhood: The Generation R study. American
Journal of Gastroenterology, 105, 4, 940-945.
Kieruj, N.D. & Moors, G.B.D. (2010). Variations in response style behavior by response scale format in
attitude research. International Journal of Public Opinion Research, 22, 3, 320-342.
King-Kallimanis, B.L., Oort, F.J. & Garst, G.J.H. (2010). Using structural equation modelling to detect
measurement bias and response shift in longitudinal data. Advances in Statistical Analysis, 94, 139156.
Knecht, A., Snijders, T.A.B., Baerveldt, C., Steglich, C.E.G. & Raub, W. (2010). Friendship and delinquency:
Selection and influence processes in early adolescence. Social Development, 19, 494-514.
Koppenol-Gonzalez, G.V., Bouwmeester, S. & Boonstra, A.M. (2010). Understanding planning ability measured by the Tower of London: An evaluation of its internal structure by latent variable modeling.
Psychological Assessment, 22, 4, 923-934.
Koretz, D., & Béguin, A.A. (2010). Self-monitoring assessments for educational accountability systems.
Measurement, 8, 92-109.
Kremers, S., Reubsaet, A., Martens, M., Gerards, S., Jonkers, R., Candel, M., De Weerdt, I. & De Vries, N.
(2010). Systematic prevention of overweight and obesity in adults: A qualitative and quantitative
literature analysis. Obesity Reviews, 11, 371-379.
Kuppens, P., Oravecz, Z. & Tuerlinckx, F. (2010). Feelings change: Accounting for individual differences in
the temporal dynamics of affect. Journal of Personality and Social Psychology, 99, 1042-1060.
Kwak, L., Kremers, S.P.J., Candel, M.J.J.M., Visscher, T.L.S., Brug, J. & Van Baak, M.A. (2010). Changes in
skinfold thickness and waist circumference after 12 and 24 months resulting from the NHF-NRG In
Balance-project. International Journal of Behavioral Nutrition and Physical Activity, 7, 26. (electronic
journal)
Lepianka, D.A., Gelissen, J.P.T.M. & Van Oorschot, W.J.H. (2010). Popular explanations of poverty in
Europe: Effects of contextual and individual characteristics across 28 European countries. Acta Sociologica, 53, 1, 53-72.
Ligtvoet, R., Van der Ark, L.A., Te Marvelde, J.M. & Sijtsma, K. (2010). Investigating an invariant item
ordering for polytomously scored items. Educational and Psychological Measurement, 70, 4, 578-595.
Lombardo, R. & Meulman, J.J. (2010). Multiple correspondence analysis via polynomial transformations of
ordered categorical variables. Journal of Classification, 27, 191-210.
Lukociene, O., Varriale, R. & Vermunt, J.K. (2010). The simultaneous decision(s) about the number of
lower- and higher-level classes in multilevel latent class analysis. Sociological Methodology, 40, 1,
247-283.
Magis, D., Béland, S., Tuerlinckx, F. & De Boeck, P. (2010). A general framework and an R package for the
detection of dichotomous differential item functioning. Behavior Research Methods, 42, 847-862.
100
6 Publications
Maij-de Meij, M.A., Kelderman, H. & Van der Flier, H. (2010) Improvement in detection of differential item
functioning using a mixture item response theory model. Multivariate Behavioral Research , 45,975999.
Manzoni, A., Vermunt, J.K., Luijkx, R. & Muffels, R.J.A. (2010). Memory bias in retrospectively collected
employment careers: A model-based approach to correct for measurement error. Sociological
Methodology, 40, 1, 39-73.
Maris, G., Schmittmann, V.D. & Borsboom, D. (2010). Who needs linear equating under the NEAT design?
Measurement, 8, 11-15.
Martens, R., Den Brabander, C., Rozendaal, J., Boekaerts, M. & Van der Leeden, M. (2010). Inducing mind
sets in self-regulated learning with motivational information. Educational Studies, 36, 3, 311-327.
Marx, B.D., Eilers, P.H.C., Gampe, J. & Rau, R. (2010), Bilinear modulation models for seasonal tables of
counts, Statistics and Computing, 20, 2, 191-202.
Matzke, D., Dolan, C.V. & Molenaar, D. (2010). The issue of power in the identification of “g” with lowerorder factors. Intelligence, 38, 336-344.
Maus, B., Van Breukelen, G.J.P., Goebel, R. & Berger, M.P.F. (2010). Optimization of blocked designs in
fMRI studies. Psychmometrika, 75, 2, 373-390.
Maus, B., Van Breukelen, G.J.P., Goebel, R. & Berger, M.P.F. (2010). Robustness of optimal design of fMRI
experiments with application of a genetic algorithm. NeuroImage, 49, 2433-2443.
Meijer, R.R. (2010). A comment on Watson, Deary & Austin (2007) and Watson, Roberts, Gow & Deary
(2008): How to investigate whether personality items form a hierarchical scale? Personality and
Individual Differences, 48, 502-503.
Mercken, L., Snijders, T.A.B., Steglich, C., Vartiainen, E. & De Vries, H. (2010). Dynamics of adolescent
friendship networks and smoking behavior. Social Networks, 32, 72-81.
Mercken, L., Snijders, T.A.B., Steglich, C., Vartiainen, E. & De Vries, H. (2010). Smoking-based selection and
influence in gender-segregated friendship networks: A social network analysis of adolescent smoking.
Addiction, 105, 1280-1289.
Middeldorp, C.M., Slof-Op 't Landt, M.C.T., Medland, S.E., Van Beijsterveldt, C.E.M., Bartels, M., Willemsen,
G., Hottenga, J.J., De Geus, E.J.C., Suchiman, H.E.D., Dolan, C.V., Neale, M.C., Slagboom, P.E. &
Boomsma, D.I. (2010). Anxiety and depression in children and adults: Influence of serotonergic and
neurotrophic genes? Genes, Brain and Behavior, 9, 6, 808-816.
Minica, C.C., Boomsma, D.I., Van der Sluis, S. & Dolan, C.V. (2010). Genetic association in multivariate
phenotypic data: Power in five models. Twin Research and Human Genetics, 13, 6, 525-543.
Molenaar, D., Dolan, C.V. & Verhelst, N. (2010). Testing and modeling non-normality within the one factor
model. British Journal of Mathematical and Statistical Psychology, 63, 293-317.
Molenaar, D., Dolan, C.V., Wicherts, J. M. & Van der Maas, H.L.J. (2010). Modeling differentiation of
cognitive abilities within the higher-order factor model using moderated factor analysis. Intelligence,
38, 611-624.
Mommersteeg, P.M.C., Kupper, N., Schoormans, D., Emons, W.H.M. & Pedersen, S.S. (2010). Health related
quality of life is related to cytokine levels at 12 months in patients with chronic heart failure. Brain,
Behavior and Immunity, 24, 4, 615-622.
Mooijaart, A. & Bentler, P.M. (2010). An alternative approach for nonlinear latent variable models.
Structural Equation Modeling, 17, 357-373.
Mooijaart, A. & Satorra, A. (2010). Testing for nonlinear relationship in structural equation modeling. UCLA
Statistics Preprint. (electronic jounal)
Mook-Kanamori, D.O., Steegers, E.A.P., Eilers, P.H., Raat, H., Hofman, A. & Jaddoe, V.W.V. (2010). Risk
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factors and consequences of first trimester fetal growth retardation. Reproductive Sciences, 17, 3,
Suppl. S, 518.
Mook-Kanamori, D.O., Steegers, E.A.P., Eilers, P.H., Raat, H., Hofman, A. & Jaddoe, V.W.V. (2010). Risk factors and outcomes associated with first-trimester fetal growth restriction. Obstetrical & Gynecological
Survey, 65, 6, 362-363.
Mook-Kanamori, D.O.,Steegers, E.A.P., Eilers, P.H., Raat, H., Hofman, A. & Jaddoe, V.W.V. (2010). Risk
factors and outcomes associated with first-trimester fetal growth restriction. Journal of the American
Medical Association, 303, 6, 527-534.
Moors, G.B.D. (2010). Ranking the ratings. A latent-class regression model to control for overall agreement
in opinion research. International Journal of Public Opinion Research, 22, 1, 93-119.
Mulder, E.J., Koopman, C.M., Vermunt, J.K., De Valk, H.W. & Visser, G.H.A. (2010). Fetal growth trajectories
in type-1 diabetic pregnancy. Ultrasound in Obstetrics and Gynecology, 36, 6, 735-742.
Mulder, J., Hoijtink, H. & Klugkist, I. (2010). Equality and inequality constrained multivariate linear models:
Objective model selection using constrained posterior priors. Journal of Statistical Planning and
Inference, 140, 887-906.
Nalbantov, G.I., Franses, P.H.B.F., Groenen, P.J.F. & Bioch, J.C. (2010). Estimating the market share
attraction model using Support Vector Regressions. Econometric Reviews, 29, 688-716.
Obermann-Borst, S.A., Heijmans, B.T., Slagboom, E.P., Eilers, P.H.C.,Wildhagen, M.F., Steegers, E.A.P. &
Steegers-Theunissen, R.P.M. (2010). Periconception maternal risk factors affect DNA methylation
profiles in very young children, with gender effects. Reproductive Sciences, 17, 3, Suppl. S, 223A.
Palardy, G. & Vermunt, J.K. (2010). Multilevel growth mixture models for classifying groups. Journal of
Educational and Behavioral Statistics, 35, 5, 532-565.
Pavlopoulos, D., Muffels, R.J.A. & Vermunt, J.K. (2010). Wage mobility in Europe: A comparative analysis
using restricted multinomial logit regression. Quality and Quantity, 44, 1, 115-129.
Peeters, C., Lensvelt-Mulders, G. & Lasthuizen, K. (2010). A note on a simple and practical randomzied
response framework for eliciting sensitive, dichotomous and quantitative information. Sociological
Methods and Research, 39, 2, 283-296.
Perperoglou, A. & Eilers, P.H.C. (2010). Penalized regression with individual deviance effects. Computational Statistics, 25, 2, 341-361.
Pieper, S., Brosschot, J.F., Van der Leeden, M. & Thayer, J.F. (2010). Prolonged cardiac effects of momentary assessed stressful events and worry episodes. Psychosomatic Medicine, 72, 570-577.
Pijnenborg, G.H.M., Withaar, F.K., Brouwer, W.H., Timmerman, M.E., Van den Bosch, R.J. & Evans, J.J.
(2010). The efficacy of SMS text messages to compensate for the effects of cognitive impairments in
schizophrenia. British Journal of Clinical Psychology, 49,259-274.
Ploeger, A., Raijmakers, M.E.J., Van der Maas, H.L.J. & Galis, F. (2010). The association between autism
and errors in early embryogenesis: What is the causal mechanism? Biological Psychiatry, 67, 602-607.
Polak, M.G, Van, H.L., Overeem-Seldenrijk, J., Heiser, W.J., & Abraham, R.E. (2010). The developmental
profile: Validation of a theory-driven instrument for personality assessment. Psychotherapy Research,
20 259-272.
Polak, M.G., Abraham, R.E. Van, H.L, Ingenhoven, T. (2010). Interrater reliability in unbalanced designs: A
comment on Scholten et al. (2009). Bulletin of the Menninger Clinic,74, 236-242.
Polak, M.G., Van, H.L, Overeem-Seldenrijk, J., Heiser, W.J., Abraham, R.E. (2010). The Developmental
Profile: Validation of a theory driven instrument for personality assessment. Psychotherapy Research,
20,259-272.
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Pratte, M.S., Rouder, J.N. & Morey, R.D. (2010). Separating mnemonic process from participant and item
eects in the assessment of ROC asymmetries. Journal of Experimental Psychology: Learning, Memory,
and Cognition, 36, 224-232.
Pratte, M.S., Rouder, J.N., Morey, R.D. & Feng, C. (2010). Exploring the differences in distributional properties between Stroop and Simon effects using delta plots. Attention, Perception & Psychophysics, 72,
2013-2025
Prins, J.T., Hoekstra-Weebers, J.E.H.M., Gazendam-Donofrio, S.M., Dillingh, G.S., Bakker, A.B., Huisman, M.,
Jacobs, B. & Van der Heijden, F.M.M.A. (2010). Burnout and engagement among resident doctors in
the Netherlands: A national study. Medical Education, 44, 236-247.
Pronk, T. & Visser, I. (2010). The role of reversal frequency in learning noisy second order conditional
sequences. Consciousness and Cognition, 19, 2, 627-35.
Roelofs, J., Braet, C., Rood, L., Timbremont, B., Van Vlierberghe, L., Goossens, L., Van Breukelen G.J.P.
(2010). Norms and screening utility of the Dutch version of the Children’s Depression Inventory (CDI)
in clinical and non-clinical youth. Psychological Assessment, 22, 866-877.
Rouder, J.N., Pratte, M.S., Morey, R.D. (2010) Latent mnemonic strengths are latent: A comment on
Mickes, Wixted & Wais (2007). Psychonomic Bulletin and Review, 17, 427-435
Rutten, R.P.J.H. & Gelissen, J.P.T.M. (2010). Social values and the economic development of regions. European Planning Studies, 18, 6, 921-939.
Schepers, J. & Van Mechelen, I. (2010). Uniqueness of real-valued hierarchical classes models. Journal of
Mathematical Psychology, 54, 215-221.
Scheres, A. & Hamaker, E.L. (2010). What we can and cannot conclude about the relationship between
steep temporal reward discounting and Hyperactivity-Impulsivity symptoms in Attention-Deficit
Hyperactivity Disorder. Biological Psychiatry, 68, e17-e18. (electronic journal)
Schilder, C.M., Seynaeve, C., Beex, L.V., Boogerd, W., Linn, S.C., Gundy, C.M., Huizenga, H.M., Nortier, J.W.,
Van de Velde, C.J.V., Van Dam, F.S., & Schagen, S.B. (2010). Effects of Tamoxifen and Exemestane on
cognitive functioning of postmenopausal patients with breast cancer: Results from the neuropsychological side study of the Tamoxifen and Exemestane Adjuvant Multinational Trial. Journal of Clinical
Oncology, 28, 8, 1294-1300.
Schmand, B., Huizenga, H.M., & Van Gool, W.A. (2010). Meta-analysis of CSF and MRI biomarkers for
detecting preclinical Alzheimer's disease. Psychological Medicine, 40, 1, 135-145.
Schmittmann, V.D., Dolan, C. V., Raijmakers, M.E.J. & W.H. Batchelder (2010). Parameter identification in
multinomial processing tree models. Behavior Research Methods, 42, 836-846.
Schok, M.L., Kleber, R.J., & Lensvelt-Mulders, G.J.L.M. (2010). A model of resilience and meaning after
military deployment: Personal resources in making sense of war and peacekeeping experiences.
Aging and Mental Health 14, 3, 328-338.
Sieh, D.S., Meijer, A.M., Oort, F.J., Visser-Meily, J.M., Van der Leij, D.A. (2010). Problem behavior in children
of chronically ill parents: a meta-analysis. Clinical Child and Family Psychology Review, 13, 384-397.
Silva, L.M., Jansen, P.W., Steegers, E.A., Jaddoe, VW, Arends, L.R., Tiemeier, H., Verhulst, F.C., Moll, H.A.,
Hofman, A., Mackenbach, J.P. & Raat, H. (2010). Mother's educational level and fetal growth: The
genesis of health inqualities. International Journal of Epidemiology, 39, 5, 1250-1261.
Smerecnik, C., Mesters, I., Candel, M.J.J.M., De Vries, H., & De Vries, N.K. (2010). Genetic health messages
in the mass media: Do the general public perceive non-personalized genetic health messages as
personally relevant? British Journal of Health Psychology, 15, 941-956.
Smits, N. (2010). A note on Youden's J and its cost ratio. BMC Medical Research Methodology, 10:89.
(electronic journal)
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Snijders, T.A.B. & Doreian, P. Introduction to the special issue on network dynamics (2010). Social
Networks, 32, 1-3.
Snijders, T.A.B. (2010). Conditional marginalization for exponential random graph models. Journal of Mathematical Sociology, 34, 239-252.
Snijders, T.A.B., Koskinen, J., & Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. Annals of Applied Statistics, 4, 567-588.
Snijders, T.A.B., Van de Bunt, G.G. & Steglich, C.E.G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60.
Spain, S.M., Miner, A.G., Kroonenberg, P.M. & Drasgow, F. (2010). Job performance as multivariate dynamic criteria: Experience sampling and multiway component analysis. Multivariate Behavioral Research,
45, 599-626.
Spreen, M., Timmerman, M.E., Horst, P.T. & Schuringa, E. (2010). Formalizing clinical decisions in individual
treatments: Some first steps. Journal of Forensic Psychology Practice, 10, 285-299.
Spreeuwenberg, M.D., Bartak, A., Croon, M.A., Hagenaars, J.A.P., Busschbach, J.J.V., Andrea, H., Twisk, J.,
& Stijnen, T. (2010). The multiple propensity score as control for bias in the comparison of more than
two treatment arms: An introduction from a case study in mental health. Medical Care, 48, 2, 166174.
Stegeman, A. & Comon, P. (2010). Subtracting a best rank-1 approximation may increase tensor rank.
Linear Algebra and its Applications, 433, 1276-1300.
Stegeman, A. (2010). On uniqueness of the n-th order tensor decomposition into rank-1 terms with linear
independence in one mode. SIAM Journal on Matrix Analysis and Applications, 31, 2498-2516.
Tan, F.E.S. (2010). Conditions for DA - maximin marginal designs for generalized linear mixed models to be
uniform. Communications in Statistics - Theory and Methods, 40, 2, 255-266.
Ten Holt, J.C., Van Duijn, M.A.J. & Boomsma, A. (2010). Scale construction and evaluation in practice: A
review of factor analysis versus item response theory applications. Psychological Test and Assessment
Modeling, 52, 272-297.
Timmerman, M.E., Ceulemans, E., Kiers, H.A.L. & Vichi, M. (2010). Factorial and reduced K-means
reconsidered. Computational Statistics & Data Analysis, 54, 1858-1871.
Timmermans, T., Van Mechelen, I. & Kuppens, P. (2010). The relationship between individual differences in
intraindividual variability in core affect and interpersonal behaviour. European Journal of Personality,
24, 623-638.
Timmers, C.F. & Glas, C.A.W. (2010). Developing scales for information seeking behaviour. Journal of
Documentation, 66, 1, 46-69.
Van Apeldoorn, F.J., Timmerman, M.E., Mersch, P.P.A., Van Hout, W.J.P.J., Visser, S., Van Dyck, R., et al.
(2010). A randomized trial of cognitive-behavioral therapy or selective serotonin reuptake inhibitor or
both combined for panic disorder with or without agoraphobia: Treatment results through 1-year
follow-up. Journal of Clinical Psychiatry, 71, 574-586.
Van Assen, M.A.L.M. & Snijders, C. (2010). The effect of nonlinear utility on behaviour in repeated
prisoner's dilemmas. Rationality and Society, 22, 301-332.
Van Assen, M.A.L.M. (2010). The incidence of strong power in complex exchange networks. Advances in
Group Processes, 27, 205-238.
Van Braeckel, K.N.J.A., Butcher, P.N., Van Duijn, M.A.J., Geuze, R.H., Bos, A.F., & Bouma, A. (2010).
Difference rather than delay in development of elementary visuomotor processes in children born
preterm without Celebral Palsy: A quasi-longitudinal study. Neuropsychology, 24, 90-100.
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Van de Sluis, S., Kan, K.J. & Dolan C.V. (2010) Consequences of a network view for genetic association
studies. Behavioral and Brain Sciences, 33, 173.
Van de Sluis, S., Verhage, M., Posthuma, D. & Dolan, C.V. (2010) Phenotype complexity, measurement bias,
and poor phenotypic resolution contribute to the missing heritability problem in genetic association
studies. PlosOne, nov, 11, e13929. (electronic journal)
Van den Berg, I., Arends, L.R. & Duvekot, J.J. (2010). Correction of nonvertex presentation with moxibustion. American Journal of Obstetrics and Gynecology, 203, 2, e15-e16. (electronic journal)
Van den Berg, I., Kaandorp, G.C., Van den Bosch, J.C., Duvekot, J.J., Arends, L.R. & Hunink, M.G.M. (2010).
Cost-effectiveness of breech version by acupuncture-type interventions on BL 67, including moxibustion, for women with a breech foetus at 33 weeks gestation: A modelling approach. Complementary
Therapies in Medicine, 18, 2, 67-77.
Van der Ark, L.A. & Bergsma, W.P. (2010). A note on stochastic ordering of the latent trait using the sum of
polytomous item scores. Psychometrika, 75, 272-279.
Van der Ark, L.A. & Vermunt, J.K. (2010). New developments in missing data analysis. Methodology:
European Journal of Research Methods for the Behavioral and Social Sciences, 6, 1, 1-2.
Van der Heiden, C., Melchior, K., Muris, P.E.H.M., Bouwmeester, S., Bos, A.E.R., & Van der Molen, H.T.
(2010). A hierarchical model for the relationships between general and specific vulnerability factors
and symptom levels of generalized anxiety disorder. Journal of Anxiety Disorders, 24, 2, 284-289.
Van der Horst, Ruud, Snijders, Tom, Völker, Beate, & Spreen, Marinus (2010). Social interaction related to
the functioning of forensic psychiatric inpatients. Journal of Forensic Psychology Practice, 10, 339359.
Van der Horst, W., Van Assen, M.A.L.M. & Snijders, C. (2010). Analyzing behavior implied by EWA learning:
An emphasis on distinguishing reinforcement from belief learning. Journal of Mathematical Psychology, 54, 222-229.
Van der Linden, W.J. & Glas, C.A.W. (2010). Statistical tests of conditional independence between responses and/or response times on test items. Psychometrika, 75, 120-139.
Van der Linden, W.J. & Wiberg, M. (2010). Local observed-score equating with anchor-test designs. Applied
Psychological Measurement, 34, 620-640.
Van der Linden, W.J. (2010). Linking response-time parameters onto a common scale. Journal of
Educational Measurement, 47, 1, 92-114.
Van der Linden, W.J. (2010). On bias in linear observed-score equating. Measurement: Interdisciplinary
Research and Perspectives, 8, 21-26.
Van der Linden, W.J., Klein Entink, R.H. & Fox, G.J.A. (2010). IRT parameter estimation with response times
as collateral information. Applied Psychological Measurement, 34, 5, 327-347.
Van der Loos, M.J.H.M., Koellinger, P.D., Groenen, P.J.F., & Thurik, A.R. (2010). Genome-wide association
studies and the genetics of entrepreneurship. European Journal of Epidemiology, 25, 1, 1-3.
Van der Molen, M.J.W., Huizinga, M., Huizenga, H.M., Ridderinkhof, K.R., Van der Molen, M.W., Hamel,
B.J.C., Ramakers, G.J.A. (2010). Profiling Fragile X Syndrome in males: Strengths and weaknesses in
cognitive abilities. Research in Developmental Disabilities, 31, 2, 426-439.
Van Duijvenvoorde, A.C.K., Jansen, B.R.J., Visser, I. & Huizenga, H.M. (2010). Affective and cognitive
decision-making in adolescents. Developmental Neuropsychology, 35, 5, 539-554.
Van Eijk, R., Eilers, P.H.C., Natte, R.; Cleton-Jansen, A.-M., Morreau, H., Van Wezel, T., & Oosting, J. (2010),
MLPAinter for MLPA interpretation: An integrated approach for the analysis, visualisation and data
management of Multiplex Ligation-dependent Probe Amplification', BMC Bioinformatics, 11:67.
(electronic journal)
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Van Ginkel, J.R. (2010). Investigation of multiple imputation in low-quality questionnaire data. Multivariate
Behavioral Research, 45, 574-598.
Van Ginkel, J.R., Sijtsma, K., Van der Ark, L.A. & Vermunt, J.K. (2010). Incidence of missing item scores in
personality measurement, and simple item-score imputation. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 6, 1, 17-30.
Van Groen, M.M., Ten Klooster, P.M., Taal, E., Van de Laar, M.A.F.J., & Glas, C.A.W. (2010). Applications of
the health assessment questionnaire disability index to various rheumatic diseases. Quality of Life
Research, 19, 9, 1255-1263.
Van Mechelen, I. & Smilde, A.K. (2010). A generic linked-mode decomposition model for data fusion.
Chemometrics and Intelligent Laboratory Systems, 104, 83-94.
Van Osch, L., Reubsaet, A., Lechner, L., Beenackers, M., Candel, M., & De Vries, H. (2010). Planning health
behavior change: Comparing the behavioral influence of two types of self-regulatory planning. British
Journal of Health Psychology, 15, 133-149.
Van Rooden, S.M., Heiser, W.J., Kok, J., Verbaan, D., Hilten, J.J. & Marinus, J. (2010). The identification of
Parkinson's disease subtypes using cluster analysis: a systematic review. Movement Disorders, 25, 8,
969-978.
Van Rosmalen, J.M., Van Herk, H. & Groenen, P.J.F. (2010). Identifying response styles: A latent-class
bilinear multinomial logit model. Journal of Marketing Research, 47, 1, 157-172.
Van Rossem, L., Silva, L.M., Hokken-Koelega, A., Arends, L.R., Moll, H.A., Jaddoe, V.W.V.K., Hofman, A.,
Mackenbach, J.P. & Raat, H. (2010). Socioeconomic status is not inversely associated with overweight
in preschool children. Journal of Pediatrics, 157, 6, 929-935.
Van Schijndel, T.J.P., Franse, R.K. & Raijmakers, M.E.J. (2010). The Exploratory Behavior Scale: Assessing
young visitors’ hands-on behavior in science museums. Science Education, 94, 5, 794-809.
Van Schijndel, T.J.P., Singer, E., Van der Maas, H.L.J. & Raijmakers, M.E.J. (2010). An intervention to
stimulate young preschool children’s free exploratory play. European Journal of Developmental
Psychology, 7, 5, 603-617.
Van 't Riet, J., Ruiter, R.A.C., Werrij, M.Q., Candel, M.J.J.M. & De Vries, H.H. (2010). Distinct pathways to
persuasion: The role of affect in message-framing effects. European Journal of Social Psychology, 40,
1261-1276.
Vandekerckhove, J., Verheyen, S. & Tuerlinckx, F. (2010). A crossed random effects diffusion model for
speeded semantic categorization decisions. Acta Psychologica, 133, 269-282.
Vanroelen, C., Levecque, K., Moors, G.B.D., & Louckx, F. (2010). Linking credentialed skills, social class,
working conditions and self-reported health: A focus on health inequality-generating mechanisms.
Sociology of Health & Illness, 312, 6, 948-964.
Vanroelen, C., Louckx, F., Moors, G.B.D., & Levecque, K. (2010). The clustering of health-related occupational stressors among contemporary wage-earners. European Journal of Work and Organizational
Psychology, 19, 6, 654-674.
Veldkamp, B.P. (2010). Bayesian item selection in constrained adaptive testing using shadow tests.
Psicologica, 31, 149-169.
Veldkamp, B.P., Verschoor, A.J. & Eggen, T.J.H.M. (2010). A multiple objective test assembly approach for
exposure control problems in Computerized Adaptive Testing. Psicologica, 31, 335-355.
Vermunt, J.K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political
Analysis, 18, 4, 450-469.
Viechtbauer, W. & Cheung, M. W.-L. (2010). Outlier and influence diagnostics for meta-analysis. Research
Synthesis Methods, 1, 2, 112-125.
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Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical
Software, 36, 3, 1-48.
Viechtbauer, W. (2010). Learning from the past: Refining the way we study treatments. Journal of Clinical
Epidemiology, 63, 9, 980-982.
Visser, A.M., Jaddoe, V.W., Arends, L.R., Tiemeier, H., Hofman, A., Moll, H.A., Steegers, E.A., Breteler, M.M.
& Arts, W.F. (2010). Paroxysmal disorders in infancy and their risk factors in a population-based
cohort: The Generation R Study. Developmental Medicine & Child Neurology, 52, 11, 1014-1020.
Visser, I. & Speekenbrink, M. (2010). DepmixS4 : An R package for hidden Markov models. Journal of
Statistical Software, 36, 7, 1-21.
Visser, I. (2010). Book review of "Hidden Markov Models for Time-Series," by Zucchini & MacDonald.
Journal of Mathematical Psychology, 54, 6, 509-511.
Visser, I., Jansen, B.R.J. & Speekenbrink, M. (2010). A framework for discrete change. In P.C.M. Molenaar &
K.M.Newell (Eds.), Individual pathways of change: Statistical models for analyzing learning and
development (pp. 109-123). Washington: American Psychological Association.
Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H. & Grasman, R. (2010). Bayesian hypothesis testing for
psychologists: A tutorial on the Savage-Dickey method. Cognitive Psychology, 60, 158-189.
Warrens, M.J. (2010). A formal proof of a paradox associated with Cohen's kappa. Journal of Classification,
27, 322-332.
Warrens, M.J. (2010). A Kraemer-type rescaling that transforms the odds ratio into the weighted kappa
coefficient. Psychometrika, 75, 328-330.
Warrens, M.J. (2010). Cohen's kappa can always be increased and decreased by combining categories.
Statistical Methodology, 7, 673-677.
Warrens, M.J. (2010). Inequalities between kappa and kappa-like statistics for k×k tables. Psychometrika,
75, 176-185.
Warrens, M.J. (2010). Inequalities between multi-rater kappas. Advances in Data Analysis and Classification, 4, 271-286.
Warrens, M.J. (2010). n-Way metrics. Journal of Classification, 27, 173-190.
Weeda, W.D., Waldorp, L.J., Grasman, R.P.P.P., Van Gaal, S., & Huizenga, H.M. (2010). Functional
connectivity analysis of fMRI data using parameterized regions of interest. Neuroimage, 54, 410-416.
Weisscher, N., Glas, C.A.W., Vermeulen, M. & De Haan, R.J. (2010). The use of an item response theory
based disability item bank across diseases. Journal of Clinical Epidemiology, 63, 543-549.
Wetzels, R., Grasman, R.P.P.P., & Wagenmakers, E.-J. (2010). An encompassing prior generalization of the
Savage-Dickey density ratio. Computational Statistics and Data Analysis, 54, 2094-2102.
Wetzels, R., Lee, M. D. & Wagenmakers, E.-J. (2010). Bayesian inference using WBDev: A tutorial for social
scientists. Behavior Research Methods, 42, 884-897.
Wetzels, R., Vandekerckhove, J., Tuerlinckx, F. & Wagenmakers, E.-J. (2010). Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task. Journal of Mathematical Psychology,
54, 14-27.
Wheadon, C., & Béguin, A.A. (2010). Fears for tiers: are candidates being appropriately rewarded for their
performance in tiered examinations? Assessment in Education,17, 287-300.
Wicherts, J. M., Dolan, C.V., Carlson, J.S. & Van der Maas, H.L.J. (2010). Raven’s test performance of subSaharan Africans; Mean level, psychometric properties, and the Flynn Effect. Learning and Individual
Differences, 20, 135-151.
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Wicherts, J.M. & Dolan, C.V. (2010). Measurement invariance in confirmatory factor analysis; An illustration using IQ test performance of minorities. Educational Measurement: Issues and Practice, 29, 3, 3947.
Wicherts, J.M. & Vorst, H.C.M. (2010). The relation between specialty choice of psychology students and
their interests personality and cognitive abilities. Learning and Individual Differences, 20, 494-500.
Wicherts, J.M. & Zand Scholten, A. (2010). Test anxiety and the validity of cognitive tests; A confirmatory
factor analysis perspective and some empirical findings. Intelligence, 38, 169-178.
Wicherts, J.M., Borsboom, D. & Dolan, C.V. (2010). Evolution, brain size, and the national IQ of peoples
around 3,000 years B.C. Personality and Individual Differences, 48, 104-106.
Wicherts, J.M., Borsboom, D. & Dolan, C.V. (2010). Why national IQs do not support evolutionary theories
of intelligence. Personality and Individual Differences, 48, 91-96.
Wicherts, J.M., Dolan, C.V., & Van der Maas, H.L.J. (2010). A systematic literature review of the average IQ
of sub-Saharan Africans. Intelligence, 38, 1-20.
Wicherts, J.M., Dolan, C.V., & Van der Maas, H.L.J. (2010). The dangers of unsystematic selection methods
and the representativeness of 46 samples of African test-takers. Intelligence, 38, 30-37.
Wicherts, J.M., Dolan, C.V., Carlson, J.S. & Van der Maas, H.L.J. (2010). Another failure to replicate Lynn’s
estimate of the average IQ of sub-Saharan Africans. Learning and Individual Differences, 20, 155-157.
Wools, S., Eggen, T.J.H.M., & Sanders, P.F. (2010). Evaluation of validity and validation by means of the
argument-based approach. Cadmo, 18, 1, 63-82.
6.3
Contributions to international English-language volumes
Arts, W.A. & Gelissen, J.P.T.M. (2010). Models of the welfare state. In F.G. Castles, S. Leibfried, J. Lewis, H.
Obinger & Ch. Pierson (Eds.), The Oxford Handbook of the Welfare State (Oxford Handbooks in
Politics & International Relations) (pp. 569-583). Oxford: Oxford University Press.
De Rooij, M. & Yu, H-T. (2010). The trend vector model: Identification and estimation in SAS. In H. LocarekJunge & C. Weihs (Eds.): Classification as a tool for research, (pp. 245-252). Heidelberg: Springer.
Dias, J.G., Vermunt, J.K. & Ramos, S. (2010). Mixture hidden Markov models in finance research. In A. Fink,
B. Lausen, W. Seidel & A. Ultsch (Eds.), Advances in data analysis, data handling and business
intelligence (pp. 451-459). Berlin-Heidelberg: Springer.
Eggen, T.J.H.M. (2010). The sequential probability ratio test in educational testing. In P. Peterson, E. Baker
& B. McGaw (Eds.), International Encyclopedia of Education (pp. 396-400). Oxford: Elsevier.
Eggen, T.J.H.M. (2010). Three-category adaptive classification testing. In W.J. Van der Linden & C.A.W.
Glas (Eds.), Elements of adaptive testing (pp. 373-387). New York: Springer.
Fox, G.J.A. & Verhagen, A.J. (2010). Random item effects modeling for cross-national survey data. In E.
Davidov, P. Schmidth & J. Billiet (Eds.), Cross-cultural analysis: Methods and applications (European
Association of Methodology Series) (pp. 461-482). New York, NY: Routledge.
Gelissen, J.P.T.M. (Ed.). (2010). Qualitative research methods: Readings on collection, analysis and critiques. London: Sage Publications.
Glas, C.A.W. & Béguin, A.A. (2010). Robustness of IRT observed-score equating. In A.A. Von Davier (Ed.),
Statistical models for test equating, scaling and linking (pp. 297-316). New York: Springer.
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Glas, C.A.W. & Vos, H.J. (2010). Adaptive mastery testing using a multidimensional IRT model. In W.J. Van
der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing (pp. 409-431). New York: Springer.
Glas, C.A.W. (2010). Item parameter estimation and item fit analysis. In W.J. van der Linden & C.A.W. Glas
(Eds.), Elements of adaptive testing (pp. 269-288). New York: Springer.
Glas, C.A.W. (2010). Testing fit to IRT models for polytomously scored items. In M.L. Nering & R. Ostini
(Eds.), Handbook of polytomous item response models (pp. 185-208). New York: Routledge Academic.
Glas, C.A.W., Van der Linden, W.J. & Geerlings, H. (2010). Estimation of the parameters in an item-cloning
model for adaptive testing. In W.J. Van der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing
(pp. 289-314). New York: Springer.
Halman, L.C.J.M., Hagenaars, J.A.P. & Moors, G.B.D. (2009). A map of European cultures: A research of
main value orientations shared by European countries population. In D.M. Bulynko, A.N. Danilova &
D.G. Rotman (Eds.), Contemporary Person’s Value World: Belarus in the project “European Values
Study” (pp. 35-52). Minsk: Belarusian State University Press.
Hamaker, E.L. & Klugkist, I. (2010). Bayesian estimation of multilevel models. In K. Roberts & J. Hox (Eds.),
Handbook of Advanced Multilevel Analysi (pp. 137-161). Taylor and Francis.
Hamaker, E.L., Grasman, R.P.P.P. & Kamphuis, J.H. (2010). Regime-switching models to study psychological
processes. In P.C. M.Molenaar & K.M. Newell (Eds.). Individual pathways of change: Statistical models
for analyzing learning and development (pp. 155-168). Washington, DC: American Psychological
Association.
Hamaker, E.L., Van Hattum, P., Kuiper, R.M. & Hoijtink, H. (2010). Model selection based on information
criteria in multilevel modeling. In K. Roberts & J. Hox (Eds.), Handbook of Advanced Multilevel
Analysis, 231-255. Taylor and Francis.
Hambleton, R.K., Van der Linden, W.J. & Wells, C.S. (2010). IRT models for the analysis of polytomously
scored data: Brief and selected history of model building advances. In M. Nering & R. Ostini (Eds.),
Handbook of polytomous item response theory models (pp. 21-42). London: Routledge Academic.
Heiser, W.J. & Warrens, M.J. (2010). Families of relational statistics for 2X2 tables. In H. Kaul & H.M.
Mulder (Eds.), Advances in Interdisciplinary Applied Discrete Mathematics (pp. 25-52). Singapore:
World Scientific.
Hox, J.J., De Leeuw, E., & Brinkhuis, M.J.S. (2010). Analysis models for comparative surveys. In J.A.
Harkness, M. Braun, B. Edwards, T.P. Johnson L.E. Lyberg, P.Ph. Mohler, B.-E. Pennell T.W. Smith
(Eds.), Survey methods in multicultural, multinational, and multiregional contexts. John Wiley & Sons.
pp. 395-418.
Kankarash, M., Moors, G.B.D. & Vermunt, J.K. (2010). Testing for measurement invariance with latent
class analysis. In E. Davidov, P. Schmidt & J. Billiet (Eds.), Cross-cultural analysis. Methods and applications (pp. 359-384). Taylor & Francis Group: Routledge.
Lee, M.D. & Wetzels, R. (2010). Individual differences in attention during category learning. In R. Catrambone & S. Ohlsson (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society
(pp. 387-392). Austin, TX: Cognitive Science Society.
Lensvelt-Mulders, G.J.L.M., Hox, J.J., Van der Heijden, P.G.M. & Maas, C.J.M. (2010). Meta-analysis of
randomized response research: Thirty-five years of validation. In Paul Vogt (Ed) Volume II: Data
collection in survey and interview research (pp. 185- 209). London: Sage Publications.
Lukociene, O. & Vermunt, J.K. (2010). Determining the number of components in mixture models for
hierarchical data. In A. Fink, B. Lausen, W. Seidel & A. Ultsch (Eds.), Advances in data analysis, data
handling and business intelligence (pp. 241-249). Berlin-Heidelberg: Springer.
Meijer, R.R. & Van Krimpen-Stoop, E.M.L.A. (2010). Detecting person misfit in adaptive testing. In W.J. Van
der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing (pp. 315-329). New York: Springer.
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Meijer, R.R. & Weekers, A.M. (2010). Appropriateness measurement: Person-fit. In: P. Petersen, E. Baker,
B. McGaw (Eds.), International Encyclopedia of Education (pp. 15-19). Oxford: Elsevier.
Mulder, J. & Van der Linden, W.J. (2010). Multidimensional adaptive testing with Kullback-Leibler
information item selection. In W.J. Van der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing
(pp. 77-101). New York: Springer.
Roelofs, E., Van Onna, M., & Vissers, J. (2010). Development of the driver performance assessment:
Informing learner drivers of their driving progress. In L. Dorn (Ed.), Driver Behaviour and Training. pp.
37-50. Ashgate Publishing Group
Rouder, J.N., Speckman, P., Steinley, D., Pratte, M.S. & Morey, R.D. (2010). A resampling test of shape invariance across distributions. In S. Kolenikov, D. Steinley & L. Thombs, (Eds.), Current methodological
developments of statistics in the social sciences (pp. 159-174). New York: Wiley.
Scherpenzeel, A.C. & Bethlehem, J.G. (2011), How representative are online panels? Problems of coverage
and selection and possible solutions. In: Das, M., Ester, P. & Kaczmirek, L. (Eds.), Social and Behavioral
Research and the Internet (pp. 105-132). New York-London: Routledge.
Sijtsma, K. & Emons, W.H.M. (2010). Nonparametric statistical methods. In B. McGaw, E. Baker & P.P.
Peterson (Eds.), International encyclopedia of education (pp. 347-353). Oxford: Elsevier.
Sinharay, S., Von Davier, M., Guo, Z., & Veldkamp, B.P. (2010). Assessing fit of latent regression models. In
M. Von Davier & D. Hastedt (Eds.), IERI Monograph Series (Issues and Methodologies in Large-Scale
Assessments, 3) (pp. 35-55). Princeton, NJ: IEA-ETS Research Institute.
Snijkers, G. & Bavdaz, M. (2010). Business Surveys. In: Lovric, M. (Ed.), International Encyclopedia of Statistical Science (pp. --). Heidelberg: Springer Verlag.
Steglich, C.E.G., Snijders, T.A.B., Pearson, M. (2010). Dynamic networks and behavior: Separating selection
from influence. In: Liao, T., (Ed.), Sociological Methodology, 40 (pp. 329-392). Boston-London: Basil
Blackwell.
Timmerman, M.E. & Ceulemans, E. (2010). The generic subspace clustering model. In Y. Lechevallier & G.
Saporta (Eds.), Proceedings of COMPSTAT'2010 (pp. 359-368). Berlin: Springer-Verlag.
Tuerlinckx, F. (2010). Weibull distribution. In N. Salkind (Ed.), Encyclopedia of Research Design. Thousand
Oaks, CA: Sage.
Van Breukelen, G.J.P. (2010), Analysis of covariance (ANCOVA) In: N. Salkind (Ed.), Encylopedia of Research
Design. Thousand Oaks (CA): Sage.
Van den Berg, S.M., Fikse, F., Arvelius, P., Glas, C.A.W., & Strandberg, E. (2010). Integrating phenotypic
measurement models with animal models. In G. Erhard (Ed.), Proceedings of the 9th World Congress
on Genetics Applied to Livestock Production (pp. 0870-0874). Leipzig: Zwonull Media GbR.
Van der Ark, L.A. (2010). Computation of the Molenaar Sijtsma statistic. In A. Fink, B. Lausen, W. Seidel, &
A. Ultsch (Eds.), Advances in data analysis, data handling and business intelligence (pp. 775-784).
Berlin: Springer.
Van der Linden, W.J. & Glas, C.A.W. (Eds.) (2010). Elements of adaptive testing (Statistics for Social
BehavioralSciences). New York: Springer.
Van der Linden, W.J. & Pashley, P.J. (2010). Item selection and ability estimation adaptive testing. In W.J.
Van der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing (pp. 3-30). New York: Springer.
Van der Linden, W.J. (2010). Constrained adaptive testing with shadow tests. In W.J. van der Linden &
C.A.W. Glas (Eds.), Elements of adaptive testing (pp. 31-55). New York: Springer.
Van der Linden, W.J. (2010). Item Response Theory. In P. Peterson, E. Baker & B. McGaw (Eds.), International Encyclopedia of Education (pp. 81-89). Oxford, U.K.: Elsevier Ltd.
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Van der Linden, W.J. (2010). Sequencing an adaptive test battery. In W.J. van der Linden & C.A.W. Glas
(Eds.), Elements of adaptive testing (pp. 103-119). New York: Springer.
Van Mechelen, I. & Van Deun, K. (2010). Multiple nested reductions of single data modes as a tool to deal
with large data sets. In Y. Lechevallier & G. Saporta (Eds.), Proceedings of COMPSTAT'2010. 19th
International Conference on Computational Statistics (pp. 349-358). Heidelberg: Physica-Verlag.
Van Rijn, P.W., Dolan, C.V., & Molenaar, P.C.M. (2010). State space methods for item response modelling
of multi-subject time series. In K.M. Newell & P.C.M. Molenaar (Eds.), Individual pathways of change
in learning and development (pp. --). American Psychological Association.
Veldkamp, B.P. & Van der Linden, W.J. (2010). Designing item pools for Computerized Adaptive Testing. In
W.J. Van der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing (pp. 231-245). New York:
Springer.
Vermunt, J.K. (2010). Latent Class Models. In P. Peterson, E. Baker & B. McGaw (Eds.), International
Encyclopedia of Education (pp. 238-244). Oxford: Elsevier.
Vermunt, J.K. (2010). Longitudinal research using mixture models. In K. van Montfort, J.H.L. Oud & A.
Satorra (Eds.), Longitudinal Research with Latent Variables (pp. 119-152). Heidelberg: Springer.
Vermunt, J.K. (2010). Mixture models for multilevel data sets. In J. Hox & J.K. Roberts (Eds.), Handbook of
advanced multilevel analysis (pp. 59-81). New York: Routledge.
Vos, H.J. & Glas, C.A.W. (2010). Testlet-based adaptive mastery testing. In W.J. van der Linden & C.A.W.
Glas (Eds.), Elements of adaptive testing (pp. 389-407). Heidelberg: Springer.
Vos, H.J. (2010). Sequential testing. In P. Peterson, E. Baker & B. McGaw (Eds.), International Encyclopedia
of Education (pp. 401-406). Oxford: Elsevier Ltd.
Wemmers, J.A., Van der Leeden, M. & Steensma, H.O. (2005). What is procedural justice: criteria used by
Dutch victims to assess the fairness of criminal justice procedures. In T.R. Tyler (Ed.), Procedural
Justice, Vol II (pp. 351-372). Ashgate, UK: Farnham.
6.4
Book reviews
Wetzels, R. & Wagenmakers, E.-J. (2010). Exemplary introduction to Bayesian statistical inference. Book
review of “Bayesian modeling using WinBUGS” (Wiley, 1st ed., 2009). Journal of Mathematical
Psychology, 54, 466-469.
6.5
Books and test manuals
Fox, G.J.A. (2010). Bayesian item response modeling: Theory and applications. New York: Springer.
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6.6
Articles in other journals
Beneken Genaamd Kolmer, D.M., Tellings, A.E.J.M. & Gelissen, J.P.T.M. (2010). Visies van mantelzorgers op
zorgverantwoordelijkheid. Tijdschrift voor Gezondheidswetenschappen, 88, 6, 344-354.
Gobbens, R., Van Assen, M.A.L.M., Luijkx, K.G., Wijnen-Sponselee, M.Th. & Schols, J.M.G.A. (2010).
Determinanten van fragiliteit. Tijdschrift voor Gerontologie en Geriatrie, 41, 4, 22-23.
Evers, A., Sijtsma, K., Lucassen, W. & Meijer, R.R. (2010). Het COTAN-beoordelingssysteem voor de
kwaliteit van tests herzien: What’s new? De Psycholoog, 45, 9, 48-55.
Jak, S. & Evers, A. (2010). Onderzoeksnotitie: Een vernieuwd meetinstrument voor organizational commitment. Gedrag en Organisatie, 23, 2, 158-171.
Klein Entink, R.H. & Fox, G.J.A. (2010). Reactietijden in de examinering. STAtOR, September, 4-8.
Polak, M.G. & Van, H.L. (2010). Validering van het Ontwikkelingsprofiel als theoriegestuurd instrument
voor persoonlijkheidsdiagnostiek. In Samenvattingen 38ste voorjaarcongres 2010. Tijdschrift voor
Psychiatrie, 52, (pp. 88-89).
Schijf, T., Van der Leij, D.A.V., Van der Berkel, A., Bekebrede, J.I., & Zijlstra, B.J.H. (2010). Spellingvaardigheden van brugklassers. Levende Talen Tijdschrift, 11, 2, 3-12.
Snijkers, G. (2010). Het verzamelen van gegevens bij bedrijven: een responsmodel [Collecting data with
businesses: a response model]. STAtOR, 4, 4-10.
Ten Klooster, P.M., Weekers, A.M., Eggelmeijer, F., Van Woerkom, J.M., Drossaert, C.H.C., Taal, E., Baneke,
J.J., & Rasker, J.J. (2010). Optimisme en/of pessimisme: factorstructuur van de Nederlandse Life
Orientation Test-Revised. Psychologie & gezondheid, 38 ,2, 89-100.
Van der Maas, H. L.J., Klinkenberg, S., & Straatemeier, M. (2010). Rekentuin.nl: Combinatie van oefenen en
toetsen. Examens, 4, 10-13.
Van Putten, C.M. & Hickendorff, M. (2010). Strijd over het rekenonderwijs of inzicht in de rekenproblematiek? Een commentaar op de reactie van van den Heuvel-Panhuizen en Treffers. Tijdschrift voor
Orthopedagogiek, 49, 2, 68-70.
Veldkamp, B.P., Verschoor, A.J., & Eggen, T.J.H.M. (2010). A multiple objective test assembly approach for
exposure control problems in Computerized Adaptive Testing. Psichologica,31, 335-355.
Vos, H.J., Kloppenburg, M. & Tomson, O. (2010). Optimale uniforme scoringsregels voor innovatieve
vraagvormen. Examens, 7, 3, 21-24.
Wicherts, J. M. (May, 2010). Onzin over het IQ van Afrikanen. Blind, Interdisciplinair Tijdschrift.
Wilbrink, B., Borsboom, D. & Couzijn, M. (2010). Spelling, grammatica, en interpunctie meebeoordelen op
examens? [Should spelling and grammar be judged in exams?] Examens, 3, 5-9.
Zeuch, N., Geerlings, H., Holling, H., Van der Linden, W.J. & Bertling, J.P. (2010). Regelgeleitete Konstruktion von statistischen Textaufgaben: Anwendung von linear logistischen Testmodellen und Aufgabencloning [Rulebased generation of statistical word problems: Application of linear logistic test models
and item cloning]. Zeitschrift für Pädagogik. Beihefte, 56, 52-63
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6.7 Software and test manuals
Kroonenberg, P.M. & De Roo, Y. (2010). 3WayPack: A program suite for three-way analysis. Leiden: The
Three-Mode Company, Leiden University. (200 pp.) + computer software
Morey, R.D. & Rouder, J.N. (2010). Bayes Factor PCL: An R package for computing Bayes factor for a variety
of psychological research designs [available on the Comprehensive R Archive Network].
Morey, R.D. (2010) WoMMBAT: Working Memory Modelling using Bayesian Analysis Techniques: An R
package. [available on http://cran.r-project.org/web/packages/WMCapacity/index.html]
6.8
Other publications
Bacher, J. & Vermunt, J.K. (2010). Analyse latenter Klassen. In C. Wolf & H. Hennig (Eds.), Handbuch der
sozialwissenschaftlichen Datenanalyse (pp. 553-574). Wiesbaden: VS Verlag.
Bechger, T.M. (2010). Principal Component Analysis for Exploratory Analysis of Multivariate Group
Differences (Measurement and Research Department Reports; 2010-2). Arnhem: Cito.
Bechger, T.M., Maris, G., & Verstralen, H.H.F.M. (2010). A Different View on DIF (Measurement and
Research Department Reports; 2010-4). Arnhem: Cito.
Bechger, T.M., Maris, G., & Verstralen, H.H.F.M. (2010). The Equivalence of LLTMs Depends on the Design
(Measurement and Research Department Reports; 2010-3). Arnhem: Cito.
Bethlehem, J.G. (2010), New developments in survey data collection methodology for official statistics.
Statistics Netherlands: Discussion paper (10010).
Borg, I, Groenen, P.J.F. & Mair, P. (2010). Multidimensionele Skaliering (Sozialwissenschaftliche Forschungsmethoden, Band 1). Muenchen: Rainer Hampp Verlag.
Brinkhuis, M.J.S., & Maris, G. (2010). Adaptive Estimation: How to Hit a Moving Target (Measurement and
Research Department Reports; 2010-1). Arnhem: Cito.
Exterkate, P., Van Dijk, D.J.C., Heij, C. & Groenen, P.J.F. (2010). Forecasting the yield curve in a data-rich
environment using the factor-augmented Nelson-Siegel model. EI report serie (Ext. rep. EI 2010-06).
Erasmus University Rotterdam: Department of Econometrics.
Gower, J.C., Groenen, P.J.F., Van de Velden, M. & Vines, K. (2010). Perceptual maps: The good, the bad and
the ugly. Research in management (Ext. rep. ERS-2010-011-MKT). Erasmus University Rotterdam:
ERIM.
Hemker, B.T., Kuhlemeier, J.B., & Van Weerden, J.J. (2010). Peiling van de rekenvaardigheid en de
taalvaardigheid in jaargroep 8 en jaargroep 4 in 2009. Jaarlijks Peilingsonderzoek van het
Onderwijsniveau - Technische rapportage. Arnhem: Cito.
Hensel, R. & Van der Leeden, M. (2010). De attitude van HBO-docenten ten opzichte van competentieontwikkeling. In F. Meijers & M. Kuijpers (Eds.), Uit de schijnwerpers, in het daglicht, van monoloog
van dialoog (pp. 195-222). Den Haag: De Haagse Hogeschool.
Hensel, R. & Van der Leeden, M. (2010). Uit de schijnwerpers, in het daglicht, van monoloog naar dialoog.
In F. Meijers & M. Kuijpers (Eds.), De attitude van HBO-docenten ten opzichte van competentieontwikkeling (pp. 195-222). Den Haag: De Haagse Hogeschool.
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Kankarash, M. & Moors, G.B.D. (2010). Cross-national and cross-ethnical differences in attitudes. A case of
Luxembourg. Working Paper Series (Ext. rep. 2010-38). Luxembourg: CEPS/INSTEAD.
Kordes, J., Smeets, P., Wouda, J. Marsman, M., Van Groen, M., Eijkelhof, H., & Savelsbergh, E. (2010).
Nederlandse 15-jarigen en de natuurwetenschappen: Hun kennis, vaardigheden en visie volgens PISA.
Arnhem: Cito.
Kroonenberg, P.M. (2010). Analyse longitudinale exploratoire à l’aide de modèles à trois voies. In J.-J.
Droesbeke & G. Saporta (Eds.), Analyse statistique des données longitudinales (pp. 129–182). Paris :
Editions Technip.
Snijkers, G. & Roos, M. (2010). Reviewing the data collection strategy [in Dutch: Herijking waarneemstrategie: Opdrachtbepaling en plan van aanpak]. Report Statistics Netherlands.
Straatemeier, M. (2010) Rekenen voor de lol, op je eigen niveau. InDruk PO, winter 2010, 4-5. Kennisnet.
Straatemeier, M., Klinkenberg, S & Van der Maas, H.L.J. (2010). Spelenderwijs oefenen en meten in de
Rekentuin. In: J. Nicolai (Ed.), Dat Telt: bouwstenen voor levend rekenwiskundeonderwijs (pp. 83-89).
Nij Beets: De Freinetbeweging.
Van der Heijden, P.G.M. & Van Gils, G. (2010). Ontwikkeling Scheepsongevallenmonitor. Ministerie van
Infrasatructuur en milieu, Rijkswaterstaat, DVS.
Van der Heijden, P.G.M., Kooiman, P. & Stoop, I. (2010). External evaluation Methodological Research
2006-2010. Den Haag: Statistics Netherlands, Division of Methodology and Quality. (11 pp.).
Van der Linden, W.J. (2010). Bayes'sche Methoden in der Statistik [Bayesian methods in statistics]. In H.
Holling & B. Schmitz (Eds.), Handbuch Statistik, Methoden und Evaluation (pp. 730-742). Göttingen:
Hogrefe.
Van Rosmalen, J.M., Koning, A.J. & Groenen, P.J.F. (2010). Optimaal schalen van interactie-effecten. In A.E.
Bronner, P. Dekker, E. de Leeuw, L.J. Paas, K. De Ruyter, A. Smidts & J.E. Wieringa (Eds.), Ontwikkelingen in het marktonderzoek: Jaarboek 2010 (pp. 177-193). Haarlem: Spaarenhout.
Van Weerden, J., Hemker, B., Oosterveld, P., Van der Schoot, F., Van Til, A., & Wagenaar, H. (2010). Het
peilingsonderzoek burgerschasvorming in de basisschool. Scholen voor burgerschap. Antwerpen:
Garant. pp 223-240.
Viechtbauer, W. (2010). Meta-analyse. In H. Holling & B. Schmitz (Eds.), Handbuch Statistik, Methoden und
Evaluation (pp. 743-756). Hogrefe.
Wagenaar, H.,.Van der Schoot, F., & Hemker, B. (2010). Balans van het geschiedenisonderwijs aan het einde
van de basisschool 4 : uitkomsten van de vierde peiling in 2008. Arnhem: Cito.
Wicherts, J.M. (August, 2010). Ben jij nou zo dom of ben ik nou zo slim? Gastcolumn. Kennislink.nl.
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7
Finances
7.1
Financial statement 2010
Receipts
The participating departments of Leiden University, University of Amsterdam, University of Groningen,
Twente University, Tilburg University, Utrecht University, K.U.Leuven, Maastricht University, and Statistics
Netherlands (CBS) contributed financially according to the number of their PhD students that participated
in IOPS on 1 July 2010. A participation fee of € 700 per PhD student mounted to a total contribution of
€ 30.800 for 44 PhD students.
The Faculty of Social Sciences of Leiden University attributes an annual amount of € 18.150.
The Foundation for the Enhancement of Data Theory donated an amount of € 600 for the winner of the
IOPS Best Paper Award.
Expenses
Above the estimate of 2010 , IOPS spent € 2.800 on course instructor fees for the following two courses:
- Optimization & numerical methods in statistics (€ 1.200)
- Analysis of measurement instruments (€ 1.600)
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7.2
Summary of receipts and expenditures in 2010
7.3
Balance sheet 2010
116
7 Finances
117